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<meta name="author" content="Carsten Schwemmer" />

<meta name="date" content="2019-07-12" />

<title>Whose ideas are worth spreading? The representation of women and ethnic groups in TED talks</title>

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) 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<h1 class="title toc-ignore">Whose ideas are worth spreading? The representation of women and ethnic groups in TED talks</h1>
<h4 class="author">Carsten Schwemmer</h4>
<h4 class="date">July 12, 2019</h4>

</div>

<div id="TOC">
<ul>
<li><a href="#replication-material">Replication material</a><ul>
<li><a href="#setup">Setup</a></li>
<li><a href="#descriptive-representation">Descriptive Representation</a><ul>
<li><a href="#gender">Gender</a></li>
<li><a href="#ethnicity">Ethnicity</a></li>
<li><a href="#combined-figure-2">Combined (Figure 2)</a></li>
</ul></li>
<li><a href="#substantive-representation-text-analysis">Substantive Representation (Text Analysis)</a><ul>
<li><a href="#create-corpus">Create corpus</a></li>
<li><a href="#run-structural-topic-models">Run structural topic models</a></li>
</ul></li>
<li><a href="#ted-talks---sentiment">Ted Talks - Sentiment</a></li>
<li><a href="#reload-data-for-analysis">Reload data for analysis</a></li>
<li><a href="#representation-descriptives-for-2016-2017-table-1">Representation descriptives for 2016 &amp; 2017 (Table 1)</a></li>
<li><a href="#density-of-ethnicity-annotations-figure-s1">Density of ethnicity annotations (Figure S1)</a><ul>
<li><a href="#topic-prevalence-effects-figure-s3">Topic prevalence effects (Figure S3)</a></li>
<li><a href="#topic-clusters-and-correlations-figure-3">Topic clusters and correlations (Figure 3)</a></li>
</ul></li>
<li><a href="#tables-for-topic-proportions-and-frex-terms-supporting-information-s2">Tables for topic proportions and FREX terms (Supporting Information S2)</a></li>
<li><a href="#regressions">Regressions</a><ul>
<li><a href="#youtube-sentiment-figure-4">YouTube Sentiment (Figure 4)</a></li>
<li><a href="#sentiment-interaction-figure-s4">Sentiment interaction (Figure S4)</a></li>
<li><a href="#youtube-dislikes-figure-s5">YouTube Dislikes (Figure S5)</a></li>
<li><a href="#regression-tables-s3">Regression tables (S3)</a></li>
</ul></li>
<li><a href="#supporting-information-s1---validation-of-image-recognition-annotations">Supporting Information S1 - Validation of image recognition annotations</a><ul>
<li><a href="#pairwise-agreement-ethnicity">Pairwise Agreement: Ethnicity</a></li>
<li><a href="#pairwise-agreement-gender">Pairwise Agreement: Gender</a></li>
<li><a href="#overall-agreement-ethnicity">Overall Agreement: Ethnicity</a></li>
<li><a href="#overall-agreement-gender">Overall Agreement: Gender</a></li>
<li><a href="#disagreements">Disagreements</a></li>
<li><a href="#calculate-accuracy-and-f1-scores">Calculate Accuracy and F1 Scores</a></li>
<li><a href="#predicting-gender-and-racial-categories-with-wru">Predicting gender and racial categories with <em>wru</em></a></li>
<li><a href="#comparison-name-based-vs-human-annotations">Comparison: Name-based vs human annotations</a></li>
</ul></li>
<li><a href="#r-environment-info">R environment info</a></li>
</ul></li>
</ul>
</div>

<div id="replication-material" class="section level1">
<h1>Replication material</h1>
<div id="setup" class="section level2">
<h2>Setup</h2>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">set.seed</span>(<span class="dv">1337</span>)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(quanteda)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw">library</span>(stm)</a>
<a class="sourceLine" id="cb1-4" data-line-number="4"><span class="kw">library</span>(reshape2)</a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="kw">library</span>(gridExtra)</a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="kw">library</span>(lubridate)</a>
<a class="sourceLine" id="cb1-7" data-line-number="7"><span class="kw">library</span>(grid)</a>
<a class="sourceLine" id="cb1-8" data-line-number="8"><span class="kw">library</span>(textmineR)</a>
<a class="sourceLine" id="cb1-9" data-line-number="9"><span class="kw">library</span>(cowplot)</a>
<a class="sourceLine" id="cb1-10" data-line-number="10"><span class="kw">library</span>(stminsights)</a>
<a class="sourceLine" id="cb1-11" data-line-number="11"><span class="kw">library</span>(sentimentr)</a>
<a class="sourceLine" id="cb1-12" data-line-number="12"><span class="kw">library</span>(ggridges)</a>
<a class="sourceLine" id="cb1-13" data-line-number="13"><span class="kw">library</span>(ggrepel)</a>
<a class="sourceLine" id="cb1-14" data-line-number="14"><span class="kw">library</span>(ggeffects)</a>
<a class="sourceLine" id="cb1-15" data-line-number="15"><span class="kw">library</span>(ggdendro)</a>
<a class="sourceLine" id="cb1-16" data-line-number="16"><span class="kw">library</span>(GGally)</a>
<a class="sourceLine" id="cb1-17" data-line-number="17"><span class="kw">library</span>(MASS)</a>
<a class="sourceLine" id="cb1-18" data-line-number="18"><span class="kw">library</span>(cowplot)</a>
<a class="sourceLine" id="cb1-19" data-line-number="19"><span class="kw">library</span>(sjPlot)</a>
<a class="sourceLine" id="cb1-20" data-line-number="20"><span class="kw">library</span>(sjmisc)</a>
<a class="sourceLine" id="cb1-21" data-line-number="21"><span class="kw">library</span>(irr)</a>
<a class="sourceLine" id="cb1-22" data-line-number="22"><span class="kw">library</span>(caret)</a>
<a class="sourceLine" id="cb1-23" data-line-number="23"><span class="kw">library</span>(wru)</a>
<a class="sourceLine" id="cb1-24" data-line-number="24"><span class="kw">library</span>(genderizeR)</a>
<a class="sourceLine" id="cb1-25" data-line-number="25"><span class="kw">library</span>(stargazer)</a>
<a class="sourceLine" id="cb1-26" data-line-number="26"><span class="kw">library</span>(scales)</a>
<a class="sourceLine" id="cb1-27" data-line-number="27"><span class="kw">library</span>(psych)</a>
<a class="sourceLine" id="cb1-28" data-line-number="28"><span class="kw">library</span>(texreg)</a>
<a class="sourceLine" id="cb1-29" data-line-number="29"><span class="kw">library</span>(arm)</a>
<a class="sourceLine" id="cb1-30" data-line-number="30"><span class="kw">library</span>(tidyverse)</a>
<a class="sourceLine" id="cb1-31" data-line-number="31"><span class="kw">library</span>(AER)</a>
<a class="sourceLine" id="cb1-32" data-line-number="32"><span class="kw">theme_set</span>(<span class="kw">theme_light</span>())</a>
<a class="sourceLine" id="cb1-33" data-line-number="33"></a>
<a class="sourceLine" id="cb1-34" data-line-number="34"><span class="kw">options</span>(<span class="dt">scipen=</span><span class="dv">999</span>)</a>
<a class="sourceLine" id="cb1-35" data-line-number="35"><span class="co">#colors &lt;- brewer_pal(palette = 'Set1')(6)</span></a>
<a class="sourceLine" id="cb1-36" data-line-number="36"></a>
<a class="sourceLine" id="cb1-37" data-line-number="37">df &lt;-<span class="st"> </span><span class="kw">read_tsv</span>(<span class="st">'ted_main_dataset.tsv'</span>)</a>
<a class="sourceLine" id="cb1-38" data-line-number="38"><span class="co"># remove talks without human speakers</span></a>
<a class="sourceLine" id="cb1-39" data-line-number="39">df &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span><span class="kw">is.na</span>(speaker_image_nr_faces))</a>
<a class="sourceLine" id="cb1-40" data-line-number="40"></a>
<a class="sourceLine" id="cb1-41" data-line-number="41">df &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(date) <span class="co"># order by date</span></a>
<a class="sourceLine" id="cb1-42" data-line-number="42">df<span class="op">$</span>year &lt;-<span class="kw">str_sub</span>(df<span class="op">$</span>date, <span class="dv">1</span>, <span class="dv">4</span>) <span class="co"># extract year identifier</span></a>
<a class="sourceLine" id="cb1-43" data-line-number="43">df<span class="op">$</span>date_num &lt;-<span class="st"> </span><span class="kw">factor</span>(df<span class="op">$</span>date) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.numeric</span>() <span class="co"># create numeric date variable</span></a></code></pre></div>
<p>This document includes analysis for a dataset of TED talk transcripts which were extracted from the <a href="https://www.ted.com/talks">TED Homepage</a>. A small number of talks had to be removed because no (english) transcripts were available or the talks solely consisted of non-verbal activities. The dataset contains the following information:</p>
<ul>
<li><code>title</code>: title of the talk</li>
<li><code>speaker</code>: name of the Speaker</li>
<li><code>date</code>: month and year of the talk</li>
<li><code>text</code>: transcript of the talk</li>
<li><code>rated1</code>: first categorization of talk (e.g. “inspiring”)</li>
<li><code>rated2</code>: second categorization of talk (e.g. “funny”)</li>
<li><code>length</code>: number of characters for the talk transcript</li>
<li><code>dummy_female</code>: a dummy variable for speaker gender (female vs male)</li>
<li><code>dummy_female</code>: a dummy variable for speaker ethnicity (white vs non-white)</li>
<li><code>speaker_*</code>: several attributes on the speaker level, including info about speakers on the TED website as well as annotation from the image recognition service</li>
<li><code>yt_*</code>: several attributes for YouTube info on the level of TED talks, including video id, video url, and comment sentiment.</li>
</ul>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">head</span>(df, <span class="dv">5</span>)</a></code></pre></div>
<pre><code>## # A tibble: 5 x 46
##   date  dummy_female dummy_white nr_speakers other_speakers rated1 rated2
##   &lt;chr&gt;        &lt;dbl&gt;       &lt;dbl&gt;       &lt;dbl&gt; &lt;chr&gt;          &lt;chr&gt;  &lt;chr&gt; 
## 1 2006…            0           1           1 &lt;NA&gt;           Inspi… Funny 
## 2 2006…            0           1           1 &lt;NA&gt;           Funny  Infor…
## 3 2006…            0           1           1 &lt;NA&gt;           Infor… Fasci…
## 4 2006…            0           1           1 &lt;NA&gt;           Inspi… Persu…
## 5 2006…            0           0           1 &lt;NA&gt;           Funny  Infor…
## # … with 39 more variables: speaker &lt;chr&gt;, speaker_description &lt;chr&gt;,
## #   speaker_ethnicity &lt;chr&gt;, speaker_face_age &lt;dbl&gt;,
## #   speaker_face_asian &lt;dbl&gt;, speaker_face_black &lt;dbl&gt;,
## #   speaker_face_gender &lt;chr&gt;, speaker_face_hispanic &lt;dbl&gt;,
## #   speaker_face_other &lt;dbl&gt;, speaker_face_white &lt;dbl&gt;,
## #   speaker_gender &lt;chr&gt;, speaker_header &lt;chr&gt;, speaker_image &lt;chr&gt;,
## #   speaker_image_nr_faces &lt;dbl&gt;, speaker_intro &lt;chr&gt;, speaker_urls &lt;chr&gt;,
## #   text &lt;chr&gt;, text_length &lt;dbl&gt;, title &lt;chr&gt;, title_lower &lt;chr&gt;,
## #   yt_caption &lt;lgl&gt;, yt_category &lt;chr&gt;, yt_channel_id &lt;chr&gt;,
## #   yt_comments &lt;dbl&gt;, yt_date &lt;dttm&gt;, yt_description &lt;chr&gt;,
## #   yt_dislikes &lt;dbl&gt;, yt_duration &lt;dbl&gt;, yt_favorite &lt;dbl&gt;, yt_id &lt;chr&gt;,
## #   yt_likes &lt;dbl&gt;, yt_speaker &lt;chr&gt;, yt_title &lt;chr&gt;, yt_views &lt;dbl&gt;,
## #   yt_comments_wordcount &lt;dbl&gt;, yt_comments_sd &lt;dbl&gt;,
## #   yt_comments_sentiment &lt;dbl&gt;, year &lt;chr&gt;, date_num &lt;dbl&gt;</code></pre>
</div>
<div id="descriptive-representation" class="section level2">
<h2>Descriptive Representation</h2>
<div id="gender" class="section level3">
<h3>Gender</h3>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">df_year_gender &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="st">  </span><span class="kw">group_by</span>(year, dummy_female) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="st">  </span><span class="kw">summarise</span>(<span class="dt">n =</span> <span class="kw">n</span>()) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="st">  </span><span class="kw">na.omit</span>() <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">freq =</span> (n <span class="op">/</span><span class="st"> </span><span class="kw">sum</span>(n))) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="st">  </span><span class="kw">ungroup</span>()</a>
<a class="sourceLine" id="cb4-7" data-line-number="7">df_year_gender<span class="op">$</span>female &lt;-<span class="st">  </span><span class="kw">factor</span>(df_year_gender<span class="op">$</span>dummy_female,</a>
<a class="sourceLine" id="cb4-8" data-line-number="8">                                 <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'Male'</span>, <span class="st">'Female'</span>))</a>
<a class="sourceLine" id="cb4-9" data-line-number="9"></a>
<a class="sourceLine" id="cb4-10" data-line-number="10">cols_gender &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'Male'</span> =<span class="st"> '#778899'</span>, <span class="st">'Female'</span> =<span class="st"> '#377eb8'</span>)</a>
<a class="sourceLine" id="cb4-11" data-line-number="11"></a>
<a class="sourceLine" id="cb4-12" data-line-number="12">p_gender &lt;-</a>
<a class="sourceLine" id="cb4-13" data-line-number="13"><span class="st">  </span>df_year_gender <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb4-14" data-line-number="14">    <span class="dt">x =</span> year,</a>
<a class="sourceLine" id="cb4-15" data-line-number="15">    <span class="dt">y =</span> freq,</a>
<a class="sourceLine" id="cb4-16" data-line-number="16">    <span class="dt">group =</span> female,</a>
<a class="sourceLine" id="cb4-17" data-line-number="17">    <span class="dt">color =</span> female</a>
<a class="sourceLine" id="cb4-18" data-line-number="18">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-19" data-line-number="19"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="fl">2.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-20" data-line-number="20"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">size =</span> <span class="fl">0.9</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> cols_gender) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-22" data-line-number="22"><span class="st">  </span><span class="kw">scale_x_discrete</span>(<span class="dt">expand =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.3</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-23" data-line-number="23"><span class="st">  </span><span class="kw">scale_y_continuous</span>(</a>
<a class="sourceLine" id="cb4-24" data-line-number="24">    <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="fl">0.0</span>, <span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.5</span>, <span class="fl">0.6</span>, <span class="fl">0.7</span>, <span class="fl">0.8</span>, <span class="fl">0.9</span>),</a>
<a class="sourceLine" id="cb4-25" data-line-number="25">    <span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb4-26" data-line-number="26">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="fl">0.0</span>, <span class="fl">0.9</span>),</a>
<a class="sourceLine" id="cb4-27" data-line-number="27">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb4-28" data-line-number="28">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-29" data-line-number="29"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb4-30" data-line-number="30">    <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.5</span>, <span class="fl">0.55</span>),</a>
<a class="sourceLine" id="cb4-31" data-line-number="31">    <span class="dt">legend.title =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb4-32" data-line-number="32">    <span class="dt">legend.background =</span> <span class="kw">element_rect</span>(<span class="dt">fill =</span> <span class="st">&quot;grey95&quot;</span>)</a>
<a class="sourceLine" id="cb4-33" data-line-number="33">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-34" data-line-number="34"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Percentage'</span>, <span class="dt">x =</span> <span class="st">'Year'</span>, <span class="dt">title =</span> <span class="st">'TED talks by gender'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-35" data-line-number="35"><span class="st">  </span><span class="kw">annotate</span>(</a>
<a class="sourceLine" id="cb4-36" data-line-number="36">    <span class="st">&quot;text&quot;</span>,</a>
<a class="sourceLine" id="cb4-37" data-line-number="37">    <span class="dt">label =</span> <span class="st">&quot;Male&quot;</span>,</a>
<a class="sourceLine" id="cb4-38" data-line-number="38">    <span class="dt">x =</span> <span class="st">'2007'</span>,</a>
<a class="sourceLine" id="cb4-39" data-line-number="39">    <span class="dt">y =</span> <span class="fl">0.7</span>,</a>
<a class="sourceLine" id="cb4-40" data-line-number="40">    <span class="dt">color =</span> <span class="st">&quot;#778899&quot;</span>,</a>
<a class="sourceLine" id="cb4-41" data-line-number="41">    <span class="dt">size =</span> <span class="fl">4.5</span></a>
<a class="sourceLine" id="cb4-42" data-line-number="42">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-43" data-line-number="43"><span class="st">  </span><span class="kw">annotate</span>(</a>
<a class="sourceLine" id="cb4-44" data-line-number="44">    <span class="st">&quot;text&quot;</span>,</a>
<a class="sourceLine" id="cb4-45" data-line-number="45">    <span class="dt">label =</span> <span class="st">&quot;Female&quot;</span>,</a>
<a class="sourceLine" id="cb4-46" data-line-number="46">    <span class="dt">x =</span> <span class="st">'2007'</span>,</a>
<a class="sourceLine" id="cb4-47" data-line-number="47">    <span class="dt">y =</span> <span class="fl">0.3</span>,</a>
<a class="sourceLine" id="cb4-48" data-line-number="48">    <span class="dt">color =</span> <span class="st">&quot;#377eb8&quot;</span>,</a>
<a class="sourceLine" id="cb4-49" data-line-number="49">    <span class="dt">size =</span> <span class="fl">4.5</span></a>
<a class="sourceLine" id="cb4-50" data-line-number="50">  ) <span class="op">+</span><span class="st"> </span><span class="kw">guides</span>(<span class="dt">color =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
</div>
<div id="ethnicity" class="section level3">
<h3>Ethnicity</h3>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1">df_year_mode &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="st">  </span><span class="kw">group_by</span>(year, speaker_ethnicity) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="st">  </span><span class="kw">summarise</span>(<span class="dt">n =</span> <span class="kw">n</span>()) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="st">  </span><span class="kw">na.omit</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">freq =</span> (n <span class="op">/</span><span class="st"> </span><span class="kw">sum</span>(n))) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="st">  </span><span class="kw">ungroup</span>() </a>
<a class="sourceLine" id="cb5-7" data-line-number="7"></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"></a>
<a class="sourceLine" id="cb5-9" data-line-number="9">cols &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'White'</span> =<span class="st"> '#778899'</span>, <span class="st">'Asian'</span> =<span class="st"> '#984ea3'</span>, <span class="st">'Black'</span> =<span class="st"> '#4daf4a'</span>,</a>
<a class="sourceLine" id="cb5-10" data-line-number="10">            <span class="st">'Hispanic'</span> =<span class="st"> '#377eb8'</span>, <span class="st">'Other'</span> =<span class="st"> '#e41a1c'</span>)</a>
<a class="sourceLine" id="cb5-11" data-line-number="11"></a>
<a class="sourceLine" id="cb5-12" data-line-number="12">ltypes &lt;-</a>
<a class="sourceLine" id="cb5-13" data-line-number="13"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb5-14" data-line-number="14">    <span class="st">'White'</span> =<span class="st"> </span><span class="dv">19</span>,</a>
<a class="sourceLine" id="cb5-15" data-line-number="15">    <span class="st">'Black'</span> =<span class="st"> </span><span class="dv">17</span>,</a>
<a class="sourceLine" id="cb5-16" data-line-number="16">    <span class="st">'Asian'</span> =<span class="st"> </span><span class="dv">18</span>,</a>
<a class="sourceLine" id="cb5-17" data-line-number="17">    <span class="st">'Hispanic'</span> =<span class="st"> </span><span class="dv">15</span>,</a>
<a class="sourceLine" id="cb5-18" data-line-number="18">    <span class="st">'Other'</span> =<span class="st"> </span><span class="dv">3</span></a>
<a class="sourceLine" id="cb5-19" data-line-number="19">  )</a>
<a class="sourceLine" id="cb5-20" data-line-number="20"></a>
<a class="sourceLine" id="cb5-21" data-line-number="21">p_ethnicity &lt;-</a>
<a class="sourceLine" id="cb5-22" data-line-number="22"><span class="st">  </span>df_year_mode <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(</a>
<a class="sourceLine" id="cb5-23" data-line-number="23">    <span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb5-24" data-line-number="24">      <span class="dt">x =</span> year,</a>
<a class="sourceLine" id="cb5-25" data-line-number="25">      <span class="dt">y =</span> freq,</a>
<a class="sourceLine" id="cb5-26" data-line-number="26">      <span class="dt">group =</span> speaker_ethnicity,</a>
<a class="sourceLine" id="cb5-27" data-line-number="27">      <span class="dt">color =</span> speaker_ethnicity,</a>
<a class="sourceLine" id="cb5-28" data-line-number="28">      <span class="dt">shape =</span> speaker_ethnicity</a>
<a class="sourceLine" id="cb5-29" data-line-number="29">    )</a>
<a class="sourceLine" id="cb5-30" data-line-number="30">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-31" data-line-number="31"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="fl">2.5</span>, <span class="dt">alpha =</span> <span class="fl">0.9</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-32" data-line-number="32"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">size =</span> <span class="fl">0.9</span>, <span class="dt">alpha =</span> <span class="fl">0.9</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-33" data-line-number="33"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> cols) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-34" data-line-number="34"><span class="st">  </span><span class="kw">scale_x_discrete</span>(<span class="dt">expand =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.2</span>)) <span class="op">+</span><span class="st"> </span><span class="kw">scale_y_continuous</span>(</a>
<a class="sourceLine" id="cb5-35" data-line-number="35">    <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="fl">0.0</span>, <span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.5</span>, <span class="fl">0.6</span>, <span class="fl">0.7</span>, <span class="fl">0.8</span>, <span class="fl">0.9</span>, <span class="fl">1.0</span>),</a>
<a class="sourceLine" id="cb5-36" data-line-number="36">    <span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb5-37" data-line-number="37">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="fl">0.0</span>, <span class="fl">0.9</span>),</a>
<a class="sourceLine" id="cb5-38" data-line-number="38">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.01</span>)</a>
<a class="sourceLine" id="cb5-39" data-line-number="39">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-40" data-line-number="40"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb5-41" data-line-number="41">    <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.1</span>, <span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb5-42" data-line-number="42">    <span class="dt">legend.title =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb5-43" data-line-number="43">    <span class="dt">legend.background =</span> <span class="kw">element_rect</span>(<span class="dt">fill =</span> <span class="st">&quot;grey95&quot;</span>)</a>
<a class="sourceLine" id="cb5-44" data-line-number="44">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-45" data-line-number="45"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Percentage'</span>, <span class="dt">x =</span> <span class="st">'Year'</span>, <span class="dt">title =</span> <span class="st">'TED talks by ethnicity'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-46" data-line-number="46"><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values =</span> ltypes)</a></code></pre></div>
</div>
<div id="combined-figure-2" class="section level3">
<h3>Combined (Figure 2)</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">p_ethnicity &lt;-<span class="st"> </span>p_ethnicity <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x=</span><span class="kw">element_text</span>(<span class="dt">angle=</span><span class="dv">90</span>, <span class="dt">hjust=</span><span class="dv">1</span>),</a>
<a class="sourceLine" id="cb6-2" data-line-number="2">                                   <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.2</span>, <span class="fl">0.5</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Percentage of talks'</span>, <span class="dt">x =</span> <span class="st">'Year'</span>, <span class="dt">title =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb6-4" data-line-number="4">p_gender &lt;-<span class="st"> </span>p_gender <span class="op">+</span><span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x=</span><span class="kw">element_text</span>(<span class="dt">angle=</span><span class="dv">90</span>, <span class="dt">hjust=</span><span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Percentage of talks'</span>, <span class="dt">x =</span> <span class="st">'Year'</span>,<span class="dt">title =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb6-6" data-line-number="6">g &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(p_gender, p_ethnicity, <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'A'</span>, <span class="st">'B'</span>),</a>
<a class="sourceLine" id="cb6-7" data-line-number="7">                        <span class="dt">align =</span> <span class="st">'hv'</span>)</a>
<a class="sourceLine" id="cb6-8" data-line-number="8">g</a></code></pre></div>
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K5xTUCewGA0/f2rY1rd4LKHNlq9YcfXp/Yc/cY81PZnAAAAAAAArkXTdGTYhKAAX57wYpFAKpXIpFKZRCIRC0UCoUBAC4QCqVRizx4SQqws5yWThocEujBsV0ECEQDGguT46MXzZjiO8Dz/1ZHzLW2drgrJnRlM5k+/Ot7Q3D7QBVQv28Fv3y//8B/7quqbhzMyAAAAAACAUZQYF0UIsW1ooyhC05SAEYhEAqFAIBIwtMNLIZ7nrSzLMHRCbKTLwnUdJBABYIzIyUqfmhjrOGIyWz7/+pTOYHRVSO7JYDJ/+uWx+uY2x0GqW9uxboIDfN/csip3QQbDOP/I6FRp/rb76ImLhSw7Hg8BAQAAAAAATzd7+pQAXx+r1dr3ixqeJyazhef5tKTJXjLpqIXnPnAGIgCMERQhm3IXtHZ0NTtUHXaqNLsPn3v9hVyaxvslhDypPeyRPaSoF1YuCJsQeOd+eXl1g1KlMVssCm+viJDAlPiYlIQYmqYnRYUlxEV9dfSbptYOx7Ucx128VlRWUbt5zWLHI4oBAAAAAADcn1wqWZqVfvT8VZ3eKBGLepZN2JjMFivLRoYGL5g9bZQjdBNIIALA2CESCV/buPyjHQd0+qdVh4+rG45/c331kjkuDMxNPMkeNjlnDzetXDAjJZ4Qkrtwdi4hSqVSo9FERUU5LQ8J8v/Bq+vP5N+6dL3Y6XDhprbOP+04sHTezPmzp1G97XcGAAAAAABwT9OmxKk02ss3SrR6g4ARiIQCmn7yooYnPGvlzBYLx/OhQf6bVy6UiEWujdZVUJIDAGOKn8L7lbxlTu8aXblZcqP4katCchO9Zw8JWb8sa2ZK/ABvIhAwuQsy3nhxpcLHy+mS1cqeuFj46Z7jKo1ueCIGAAAAAAAYFfMzpq1bOi/Y31ciFuqNRp3eYDSbDUazTmc0ms0yqWRqQuzrm1cG+itcHanLIIEIAGNNTETIqsXO9YYHT18Zz+0+jCbzP7463kv2cHn27OlTBnu3SRPD/+/rmzJ6W/i4uuGDT/cWFj0ceqwAAAAAAACjLiU+Zvsr6xbPSUuMjfLxlktEIqlENCHQLzMtaeu6nJfWLhmfRx/aYQszAIxBc2ckt7R1XndIY7Ect+vg2R9uW9+zdG7MM5rMn351vK6p1XHQVns4hOyhjUQs2rA8e3J0+IFTV/Td29QYTeb9py6X1zTkLcuSSSVDjxsAAAAAAGAUScSiOTOS58xIJoSUlT8O8PcPCMA570+gAhEAxqa1S+fFRoY6jmh0+s/3n7ZYrK4KySX6yh6mJT3nzVMTYn/8xgtT4pxPSySElDyq/ODTfY8qap/zIQAAAAAAAEafWCR8VkOV8QkfCwAYmxiafiVvqb/C23GwsaV9z7EL/LPWjDlGk/nTPb1kD9cNR/bQxksu3bZpxYbl2SKhc0m7Rqf/fN/J/acum8dZ0hYAAAAAAGCMQQIRAMYsmVTyyoZlTomtktLKS9eLXRXSaHqSPWzsJXuYOUzZQ/s9M6ZP+dHrmyZGhDhd4gkpLHr4x8/31ze39boWAAAAAAAA3B8SiAAwloUFB2xevYjqPnjyYuHDsb611mQy/6O37OHa4c4e2vn7+rzz0urcBRkM7fyTpa2j689fHDpxsZDluJF4aAAAAAAAABhRSCACwBiXEh+zcE6a4wjP818ePtfSrnRVSCPNZDJ/uud4bW/Zwzkjkz20oWl6Qeb07a+sC/L3dbrEcdzFa0Uf7zzc1tk1cgEAAAAAAADASEAXZnB25WbJ0XNX7X/993e2+Pv6DGThodNXrt55YPszTVG/+ve3nj+YnQfP3CutIoT825ubgwKcUxIAA7QsO72lrfPB4xr7iMls+eLA6R9sy5OKRS4MbCSYLdZ/fn2ql+zh0nkjmj20iwgN+n++u+Fs/u1L14t5vtuBk3VNrf/72f4VCzLmpqdQz1oPAAAAAAAAbgYViNAPW/6uXzzP3yuvHuFYAIaIoqgX1yyeEOTvONjeqdp18CzHj6mWKmaL9bO9J6rqmhwHn2QPZySPWhhCgSB3Qcbrm3N9vOVOlyxW65FzBf/Yc1yt0Y1aPAAAAAAAAPA8kECEfpSUDSiBWNfYqtHqRzoYgCETi4SvbVwul0ocB8ur609euO6qkIad2WL9Z2/ZwzWjmz20mxwd8X/feCEteXLPS+VV9R98uvfOg8ejHxUAAAAAAAAMFhKI0I+6xlaVWtvvtHsDyzMCuJC/wvultUvo7i0+LhXevXH3katCGka27GFlb9nDua7IHtpIxaIXVy/aui5HJhE7XTKYzF8dOf+vQ2f1RpNLYgMAAAAAAIABGjtnIKpUqt27dxcWFnZ1dQUEBMyZM2fLli0ymWyk19oZjcY333xz3rx527dvH9IzcDveXjJbUeG9sup56Sl9zOQJuVdW7bgEwD1Nig5ftSjzyLkCx8FDZ/JDAv0jw4JdFdXzM1us/9x3spfsYc5cF2YP7VITY6MjQvaduFhaWed0qeRRZV1j6wurFsZFhbkkNgAAAAAAAOiXWycQt27dqtFo+pjw+9//ftKkSYSQjo6Od999t729nRAikUhaWloOHjx48+bN3/zmN15eXn0/ygDXFhcXf/nll5WVlYGBgVlZWZs2bRIKhY73OXLkiMFg2Lx585Cfr7tJiI0suv/YyrL3Siv7TiA2t3V2dqkJISmTo+19VADc07z0lJb2zsLip1WHViu7Y//pH76Wp+hxYJ9HsNiyh7WNjoNPsocz+/rKHU3eXrLvvJB7o+jhkfNXLRar46Uutfbvu4/Omj5lzeI5QqFb/1QCAAAAAAAYnzx7C7N9K+Inn3zS3t4eGxv7pz/96auvvvrggw+Cg4Pr6+t37NjR700GsvbChQv/+Z//WVFRMWnSJJPJtHv37v/6r/9y7C6q0+n279+/cuXKgICAYX+ariIWCSfHRBBCquubNbq+6grvf9toJTkhZjQiA3g+65ZlxUSGOo5odPodX5+yWK3PWuK2npU9XO1O2UMbipCM6VP+z2sbwkMCnS7xhBQWPfzjjgMNLe0uiQ0AAAAAAAD64Na1Hn/84x85jus5XlZW9t5776Wnp8fExBBCVCpVYWGhSCT66U9/OmHCBEJIXFzcz372sx/96EeXLl168803RSLRsx5iIGvNZvNf//rXkJCQ9957z9/fn2XZTz755MSJEwUFBfPmzbPd5+DBg1arddOmTSPygXCdlPiYh49reELul1VnpiU9a5rtAES5TBIdEdLvPTuU6tYOZVV1fXl9u7+vj8JbHhLo7+01uA3jjjier21oae3o0hmMPl4yf1+fyNAgAcMM+YYw5jE0vXVdzh93HHA837Ohpf3AycubVy9yYWCDZcseVnTPHpL/n707DY+yyvP/f6qyVFLZk0ogZCEkEJYkmASMLGEVBAREQFFBQR0UULvH6WnHq3um5/q3c82vnbEvh57e1GlbkR00gICBBmVrFmOAhDUEUglZyJ7KWpWllv+DW2NR2aqyVVV4vx6RU+fc9amQ5c63ziLEwlkp0x2setguJMj/9Ree/Obc5W/OXbI4AruiSvPHz/bPnZo0d3qyXCazV0IAAAAAgAWHLiAGBgZ2bGxtbd26dauvr+9PfvITmUwmhDhz5ozBYEhKSpIqgJLo6Ojo6Gi1Wp2ZmTlt2rSunsKasQUFBU1NTWvXrpXyuLi4rFu3Lj09/dq1a1IBsb6+/sCBA0uXLvXz8+vHl+8Ixo8ZKZfLjUbj1VvqrgqIVZq6ssoaIUTcmKju/+ZXF947cSHrdn6xRbtcJosfFz1nalJocCf/490wGI0XLt345vylJm2zebu3l+fUpLgZKRPdWQ6JLnh7ea5b8dift39pvpz20vXbocOCZjw80Y7BrNdV9XDRrJRZUxLtEslKcrl8XuqksdERuw+dqNLUmT9kNBqPn72Ym1+0askcVcBQ+4kKAAAAAE7K+ZYwb9u2raSk5Kc//WlAQIDUcvXqVSFEUlKSRc/k5GQhRHZ2djdX6/VYmUxmMBikf6elpcnl8uXLl9v0QpyC0kMhnWyQX1japGvutM/13ALpH/Gx3a1fzsi6+dHOQx2rh0IIo8l05Wben7fut2n1Ymtr2yd70g9+fc6ieiiEaGzSHft75kc7DjY26ay/IB40I4apVi2ebVHz/urEt7fyCu0TyBZtbfpPv+h87qGDVw/bRYwI+ceXV06fFN/xbYfCexW/++SLs5nX7BALAAAAANCBk83Pys3NPXDgwIwZM1JSUtobNRqNEGLECMsTPENDQ4UQtbW13VzQmrFRUVFKpXLfvn0pKSlBQUEGg2HLli0mkykhIUG6wqFDh1auXOnj49P3F+iA4seOul1QbDSZbtwueHjiuI4drufmCyEUCveYkWFdXURT3/jlD+feBvr7PjxxrDC2DQsJbm0z5N29d/nGbb3e0NqmP3r6u5efXmRNKpMQuw5+c+duifRh4oTRoyJCg/x9qzR1d+6WXLuVL4QoLqv8ePfhN15c4SJ3vkI5BkfC2OiZjzx06tsf3yowmUw7Dn7z+vPLQlQBdgzWve+rh3c7qR7OdpLqocTN1XXpvGljYyI//+pk/f0HuLe16Q9+fe5WftFTi2b59mGLAwAPuLq6up07d2ZkZNTW1gYFBU2dOvXZZ59VKq36qdKXse2am5vXr18/ffr0TZs29eoVAAAAOARnKiCaTKZPPvlEJpOtWbPGvL2urk4I4eVleXyqdIay9GhXrBnr7u6+cePG999/f9OmTWPGjCkvL6+oqEhOTk5NTRVC7N27193dfdmyZVa+CqPR2Nzc+VQ+o9Got+UMB+kUF1tH9ah930npymNHhcuEMAlxJUedNGG0Ref6hqbCexVCiPHRESaT0WD4cc9K81SXrt7S6w1CiNCQoFeeXewil927d0+lUnl6esbHRk0cH/3X3V+ZhCgqKW/T682nI7WfVGMwGMwveOVm3o07d4UQXkqP55fNixgRIrVHhQ+bnBB7O27M7sMnm1taSytrzn53ddqkuP765EjTTi3COIX25DJn21pOSq7X603375fXX+ZNTy6vrMlRF7W3tLS0fpb2tw1rlnoqutw+1RrSt1K/f6m0tem37j+mLiy1aJ8/Y3Lq5Ph+eboBSt6V6Ijhr7/w5IHjZ2/cvmvxUK666HeffL5s/vQJo0dacymj0WgymZzu21MIYTKZSD7ITCZTv/8CHTS2Ju90R2nntWbNmoaGhm46vP/++6NHjxZCVFdXv/XWW1VVVUIIDw+P8vLy/fv3Z2Zm/vd//7d0p9cNK8dmZ2fv2rVLrVarVKrU1NSnnnrKzc3N/DoHDx7U6XSrVq3q9esFAABwBM5UQLx06dL169cXLFhgMWFQKvN1vBG0voDY49jZs2f7+/vv2rXrzp07QUFBzz333NNPPy2TySorK48cObJ69Wrr345uaGg4c+ZMx3Y3N7f6+vqioqKOD3WvpqampqbG1lHdkGZlCiEaGxulPMODA0orNXkFJXfy1Ar3+26Lr9/5PnBIgHdRUZF5icf8tdzJ//7fCaPDy8u+L3xIN+VCCFchAv19qmsbdC2tt3LveHkq2gdqtd9PSiotLdU11Uv/Nglx4vxl6d8zJ40XhhaLz5uHq5iaGHvi22tCiFPfZoWH+PZvzayioqJfrzd4Skstq07O4t49y9l2/Wha4pjyqhpNfVN7S5Wm7rPP0xfOSOr7UR69+Kbuhl5vOPL3rJIKy2/5lIljokMD+ve5+vdqPZqRFBum8jt98WZLa5t5e5O2eceBr6Mjhs2cPEFh3a6mg5y8Hzlv8vaf1c6lra2tqamp536Op76+vr6+3vr+A/QGjMOS/7Dy4KOPPqqqqoqOjv7nf/7n8PBwtVr9m9/8pri4+LPPPnvttde6v4g1Y0+ePPn+++97eHhIbzDv3Lnz5s2bv/71r9vfq2tqakpLS3v88ceDgoIG7vUCAAAMAqcpIJpMpq1bt7q5uT3zzDMdH+pmYFtbWzePWj82MTExMdFybeCePXu8vLyWLFnSzUUseHl5TZkypWP75cuXvb29hw/v+RTjdiaTqby83M/Pz9PT0/pRPSoo+76AqFQqpTwPTRhdeuo7o8lU29T6UGSEeee/nb8qhHBzdZ380AQ3N1fzOQ7mr2XmI2Jyc4sQYnTkCHd3N6PRWFFRERAQoFB8Xyv08/Gurm0QQgQHB5svV/TwuCX9Q6VSqQK/P1GhSlNXqakXQoQNU6UkdX7U7LDhw6/cKqyurW/StSg8vAL9+2eBuV6vr6qqCgwM7OZob8fU0tKi0WhUKpWrq9N810uam5tra2tDQkLkA7kUfc2yeX/Zk97c0treUlRWfTO/7NFplhukWk+r1dbX19v0Td29Nr1+18GTHauHc6cmzng4ob+eRQjR2Nio1WpDQkL68ZrWGD58+IRxow8cO1dQXGbxkLqovKa2cdlj06PChnU6VlJfX9/a2qpSqQYy5oCora01Go2dHh3m4DQajUwm8/f3t3cQm1VVVbm5uTnj6WcVFRVKpbLHCXTmjEbjjRs3Bi7SIPv973/f6ZzK3Nzcd999d/LkyaNGjRJC1NXVZWRkuLu7/+IXv5DOyouJifnlL3/55ptvnj59ev369d38KrdmbGtr64cffjh8+PB33303MDDQYDB89NFH6enp586dkw7ZE0Ls379fr9c/9dRTA/KJAAAAGEROU0q4ePGiWq2ePXt2x78M/f39dTpdx0kEjY2NQoj2s1Y61ZexZWVlx44de/nllz08PIQQWq1269atmZmZWq02NjZ27dq10v2rBVdX107/uJXJZK6urjaVAqXqp5ubW/8WENuX3rTnSYqLPXLqOyFEbn7JlOQfC3ZaXfPd4nIhxNjoCF9fH3H/IinzVOPH3PepkBaluru7S30am3RlVd9XLT08PMwHuri4dGwvv/39GRejR4V389ojw4ZV19YLIapqG8JC+6cU0traKoRQKBTSf7rT8fDwsFha5fikLyoPD4/2L4aBEO7puebJeZ/sPWL+Nfz3zGvhoSGJHVbuW0l6B6K/vj3b9PrtX36jLrKcQ7pg5sNzpva+ytmp5uZmmUzWvz9YrOTp6blhzRPnMq+ln/xW/8NBVZLahqbPvvjbtEnxi+Y84trFF4NWq21ra7NL8j6S1mM6Y/K6ujq5XO6MyeVyua2/dh1EL24YhtgS5k5L7a2trVu3bvX19f3JT34iTQA8c+aMwWBISkqSKoCS6Ojo6OhotVqdmZk5bdq0rp7CmrEFBQVNTU1r166V8ri4uKxbty49Pf3atWtSAbG+vv7AgQNLly51xjo1AACABacpIB49elQIMXfu3I4P+fv7l5aWdiwCSi09FhB7PXbnzp3+/v4LFy4UQrS0tLz11ltFRUURERGBgYFZWVlZWVm/+c1vxo3r5NQRp+Pn4xUxIqToXkVufnFLa1v7KuabdwqNJpMQIm5sd+cvd2Q0meqbdE2FpZr6ptyC4vyiUmmHRCtVar5fWn7qQtapC1k99m9ubrEpHh5MY6LCF81KOXzignnj5+mnggJ8I/qpAN1reoNh+/7jdwpKLNofG4Dqod3JhJg+OX50VNjuQyfu3X8yu0mIsxev3Sm898ySOSNCOl8PaHzAlmoCkGzbtq2kpOTf/u3f2m/erl69KoRISrL8IZmcnKxWq7Ozs7spIPZ6rEwmM/zw5kdaWppcLl++fHmvXhAAAIBjcY4Cokaj+e6771Qq1cSJEzs+Kq2c6ri5W3l5efujXen12KKiopMnT27cuFFa/7Jv376ioqL169cvXbpUJpMVFBS8/fbbH3zwwebNm615gY4vIXZU0b0KvcGQk1f40PgYqfFabr4QwkUuHxcTac1F7haXZeeo7xQUV2vqDX2YDaHTdX4KTVeaW7tbxg60m5EysaK69rsrOe0ter3hs7S//WTtcl8fy6OWBo3eYNi271hOXqFF+2MzJs8dctXDdsNUAa+vffLEucvfnLtkURMsr6z502f756dOmvHIQ9ImlXl372XdvHOnoKS+scloNHkrPSNHhMSNHfXQ+BgOYQceBLm5uQcOHJgxY0ZKSkp7o7Sns8XG2UKI0NBQIURtbW03F7RmbFRUlFKp3LdvX0pKSlBQkMFg2LJli8lkSkhIkK5w6NChlStX+vj0zyYqAAAA9uUcBcTjx48bjcbZs2d3uglaQkLC+fPns7OzFy9ebN6elZUlhIiP73yPvD6O3bFjR3Bw8Pz586UPr169qlKppOqhECIqKmrevHkHDx7UarXWn6/iyOLHjvrq5LdCiGu38qUCYktr2+38YiHE6KiwHg+rbW3T7zl0Qio4Slzk8qAA3xBVQHCg/5iosG/OXb5z13J2VVeMxu+rCXFjokK7mIVkLjw02MorA8vmTy+rqim69+M5OQ2N2m0Hjr/63JKuls0OqO6qh9OSBz/PYHKRy+elToodFb778IlqzX3nRegNhvRTGTfu3F04+5Gv/37R4qdHQ5P2+u2C67cLvjl3acXCmdERoYMbHMCgMplMn3zyiUwmW7NmjXm7dBSel5fl2z/WH7LX/Vh3d/eNGze+//77mzZtkg5RqaioSE5OTk1NFULs3bvX3d192bJlfXx1AAAADsI5Cohnz54VQkyaNKnTR2fMmPHxxx9nZ2fX1dW17zJTVlaWm5urVCo7PbGkj2PVavXZs2d/+tOfmp9H0ekGQ0Nm16FAf98RIUH3Kqpz1IVtbXo3N9db6iJph7L42J7XL+88cPxmXqEQws/Ha1pyXMzIES26xtDhw9urqyetWIncztPj+6NXwkKDh/AMLNiFq6vL2hWP/WHLvrqGH3c2KCwpTztyZtXi2YMcxmAwbu+sejj/AagetosMG/bTdSsOn/w2I+umxUN3S8o/3HFQdL1muaqm7uNdh1csmjkpPnaAYwKwm0uXLl2/fn3BggUWEwalMl/H02asLyD2OHb27Nn+/v67du26c+dOUFDQc8899/TTT8tkssrKyiNHjqxevdr6d5Hr6urOnDnTsd3Nza22tjY/P7/jQ+aqq6urq6utfC5HQ3h7qampqamxPJnNWRDeXpw6vEajkSaYOyPC28sQDt/9kcKdcoICYm1trVqtdnFxGTNmTKcd/Pz8UlJSzp8//9vf/vYXv/iFUqmsq6t79913TSbTnDlzzI/Y27t3r16vnzhxYlxcnK1jze3YsWPEiBFz5sxpb0lISLh69WpaWtqKFStkMplarT5+/Hh0dLRNhyQ6uPixo+5VVLe16W/lF8XHjpKmE8pksvFjRnY/sPBehVQ9VAX6vfbCk0oPhcFgKCy8b99Jm/ZA9Pf9/rNaWd3d+iOgd3y8lGtXLvhg25dten1746VruWHDVdMndTejuX8ZDMZt+/52s7Pq4aMPTPVQolC4r1gwI270yM+PnG5o1N73WE+/9gxG4xfpp/19vGNGWi5FBDAEmEymrVu3urm5PfPMMx0f6magdM5VN5e1cmxiYmJiYqJFhz179nh5eS1ZsqSbi1jw8PAYP358x/a8vDxPT8/uj2ivqalRKpVOerybs4f38vJSKBT2DtIbGo3G09PTST/zTh3e2b/mnTe8RqOxOK7TiRDeXoZ2+F5Md3OCAqK0mjgmJqarcp4Q4tVXX83Nzc3Ozl6zZk1YWFhRUZHJZIqMjHzhhRfMu+3du7e5uVkul7cXEK0f2y43NzcjI+PnP/+5+Zmwy5cvP3PmzJYtW44ePerj45OXlyeTyV577bW+vnhHEj82+m9nMoUQ127lj4uOvJVXKIQYFT7cW9nDt1PBD+fGJo4frfTo5B5LbzAUX0WN1gAAIABJREFUlVZanyQyLKT9ynqDodOFpSYhvkg/pdW1yOWyZ5fOtcviUzivsGGq5Qtn7Dl0wrzx8NfnVQF+Y6MjBiFAl9XD1AeuethubEzkP760Mi399I07d20aaDQaP08/9c+vrOLnADD0XLx4Ua1Wz549W6VSWTzk7++v0+k6HpTX2NgorDhkr9djy8rKjh079vLLL0t/Y2u12q1bt2ZmZmq12tjY2LVr144a1cnSDYVCERMT07E9Pz9foVB0f45zTU2Np6enr69vN30clrOH9/DwcNLw0h+WTnpEOOHtxanD19bWEt4uCG8vPYbvRQHRCXaXlwqIEyZM6KZPUFDQ5s2bFy1a5O/vX1JSEhwcvGLFivfee8+alSO2jt22bVtkZKS0wU07hULx3nvvLV682GQylZaWJiUlbd68OTZ2SC2aCwnyDwnyF0LcvHM3R13Y0tomrDt/ubmlVfpHV1+gp7/NNp/q1aOwYSo/Hy8hhKa+8fzF6532yckrzLxy68btgrY2PVUD9EJy3JgZD993apPRZNp98BuLzfgGgsFg3Lb/WOfVw+kPaPVQ4q30XLtywarFs93cbHv3S1PXkHnl1gClAmBHR48eFULMnTu340PSUXgdi4BSS48FxF6P3blzp7+//8KFC4UQLS0tb7311uHDh93d3cPDw7Oysn72s5/l5OR0MxwAAMAxOcEMxDfffPPNN9/ssZufn9+mTZs2bdrUTZ89e/b0emy7d955p9N2pVK5YcOGDRs2WHMRJxU/Nvqbc5daWtsOf3Ph+5YxUT2OGqb6/j770vXbU5PjfLx/rMxqm1tOfZt9ymwDxCpNnV9Px93K5fJpk+LTT34rhDh29mKgv29c7H0xKmtqv0g/Jf07Oa7zle9AjxbNeaS8qiY3v7i9Rdvc8lna0ddeeFLh7jZAT9rVqSnzUic94NXDdsnxscXlVecyr9k06mqOekpSd29EAXA6Go3mu+++U6lUEydO7PioVAQsLS21aC8vL29/tCu9HltUVHTy5MmNGzdK62b27dtXVFS0fv166Zy9goKCt99++4MPPti8ebM1LxAAAMBxOMEMRDiO+B/mG2rqGoQQEaEhfr49b/IYGTbMRS4XQtTWN773f7t3Hzpx/Oyl89m5uw+dfPdP26XqYaCfj9T588MnvzpxocdJXqmTE0YMUwkhWlvbtu7727Z9x85fun7nbsnVW+ovj5/730/TGpt0Qoi4MVETx3eyIAiwhlwme+6JR1WB9836Lq/S7D74TS92nLVGl9XD6ZPmTe/8FKkHU2WVzfuf3i0pH5D/MwD2c/z4caPROHv2bLm8kxvahIQEIUR2drZFu7S0JT6+uz1tez12x44dwcHB8+fPlz68evWqSqWSqodCiKioqHnz5qnVaq1W29UVAAAAHBMFRNggNCQo0P/HfV7i75/315UAP58Fs1Kkf7e2tl2+fvvE+ctXbt29lV/c2qaXyWRPzJ/+3LJH5TKZEEJT33g644pUoOyGi4v8xZULQn6Y23gtN//AsbN/2XV4+/7j5y5ea2vTCyHChwc/s3SudMsO9I6nh2LdigUeivs2YL1x5+6xM5n9/lxdnbn86PTkealUD+9T32i5rrBHeoOhSasbiDAA7OXs2bNCiEmTOv8JOWPGDBcXl+zsbPMDl8vKynJzc5VK5ZQpU7q5cu/GqtXqs2fPPvvss66uPy7x6XT/ll7sOgQAAGBfFBBhA5nZJEQhRFxszxsgSmY8nPDkY6kdj1sZFT78jbXLpyXHRYSGPL98fkiQv5uba2hIkNKz5/PsfH28Xn9+2awpie4ddkPzUnoseyx10wvLOj4E2Co4yH/1snny+yvRJ85fzr6Z14/PYjAYtx843nHfw1mPPDQ/dXI/PtHQIO/VGwO8nQAMJbW1tWq12sXFZcyYzvcq8fPzS0lJ0el0v/3tb6UZf3V1de+++67JZJozZ4750Xx79+7duXPn9evXezHW3I4dO0aMGDFnzpz2loSEhJqamrS0NGneulqtPn78eHR0tLd3zws4AAAAHArlFVhKnZyQOjmhq0cfn/3I47Mf6epRuVz+7tuvdmyXyWRTkiYkx40pLq+q1tQ1NGrbWpvjx8aEhQ5r7zNhTNSEDjsqPv/k/G6iKhTui2alzJ8+SV1UWl1b39zSGujrrQryDwnyd3Plaxv9JnZU+IKZD6efymhvMQnxefqp4EA/aSl9HxkMxu37j3U8XHjutOTHZlA97ISPj1dpZY1NQ9xcXZWeHgOUB8Dgk1YTx8TEdFXOE0K8+uqrubm52dnZa9asCQsLKyoqMplMkZGRL7zwgnm3vXv3Njc3y+XyuLg4W8e2y83NzcjI+PnPf+5idnTb8uXLz5w5s2XLlqNHj/r4+OTl5clkstdee62vLx4AAGDQUWTB4HF3d4uOCI2OCDUYDIWFhQE/7HvYR66uLrGjwvvlUkBXZk5JLK2sybpxp72lrU3/Wdrf3li3vOPUWpt0WT2cmkT1sCsxEaG56iKbhoyKDGX+ITCUSAXECRO6OxwpKCho8+bNO3bsyMjIKCkpCQ4OTk1NfeaZZzw9e/65bevYbdu2RUZGpqammjcqFIr33ntv69atmZmZpaWlSUlJL7744siRI215oQAAAA6BAiIA9EwmxFOLZlVr6otKK9oba+sbt6b97ZXnlriazTexicFo3HHgeMfq4cyUiY/NfLj3cYe6+HHRR09/Z7TlKJv4sVEDFgeAHbz55ptvvvlmj938/Pw2bdq0adOmbvrs2bOn12PbvfPOO522K5XKDRs2bNiwwZqLAAAAOCz2QAQAq7i6uqxd8Zivj5d5492S8n1Hz/Tuggajccf+49dvF1i0z0yZ+Pic7nb3R5C/76SJY20acvnaneaW1gHKAwAAAABDGwVEALCWj7fy+WXzLOYbXryae/7yDVsvZTSZ9hw60bF6OIPqoXUen/2IKtDP+v75RaV/3v5lfaN24CIBAAAAwFBFAREAbBAZNmzFwhkWjQePn8u7e8/6ixhNpt0Hv+l4jvOMhycupnpoHU8PxUtPLQoK8LV+SHllzQfbv6yurR+4VAAAAAAwJFFABADbJMfHTp8cb95iNBq37z9mZWXKaDLtPnSi8+rhXKqHNggK8H197fKkuDEyWefnowT4elu01NTWf7D9S1tPcAYAAACABxwFRACw2eK5U8dGR5i3aJtbtnx+pKWnXfa+rx6aneYsSX04gephLyg9FM8smfPmSyvnTE2KGBHirfRQeihCgwNTEsf/w6rH3960evljqRblxYZG7Uc7Dt4tKbdXZgAAAABwOpzCDAA2k8tkzy6d+4fP9lVrfpx1WFFdu+fwyeeXz+9qQpy072Gn1cMlc6cOYNyhblhw4ILgwAXi4erqap1OFx4e3v7QI0kTvL2UO7/8Wm8wtDfqmls+3n34+Sfnx95fBQYAAAAAdIoZiADQG54eihdXLvRQuJs3Xr9d8LczmVdz1J+nn9r+5Tf7vs7Y8sXRv53JLCmvkqqHWR2rh5OpHg6suNiol55epHB3M29sbdNvSTt6JUdtr1QAAAAA4ESYgQgAvRQc5L/6iUc//fyI0WRqbzx5/rLJrE9Fdd3NO3e/OXfJ10tZ32R5BHDq5IQlj1I9HHAxI0e88uyST/amN+ma2xsNBuOuL7/WNbc8kjjejtkAAAAAwPExAxEAei82OmLejMnmLaYuelI9tK/w0OANq5f6+XiZNxpNpn1Hz6SfyrBXKgAAAABwChQQAaBP5kxNemh8jK2jHkkcv5jq4eAKUQVsXPOEKsDPov3Uhawvj58zmbqq/QIAAADAg44CIgD0iUyImKgRNg2ZPHHskwtmdH7SCgZSgJ/PxjVPjAgJsmg/d/HansMnjUajXVIBAAAAgIOjgAgAfWIwGr85e9mmIUEBflQP7cXby/OV1UujwodbtF++fnv7/uN6vaHTUQAAAADwIKOACAB9kl9YWlvfaNOQy9dvD1AYWMNT4b7+2cVxsVEW7ddvF/x1z1ctLa32CAUAAAAAjosCIgD0ye27JbYOqajS1Dc0DUQYWMnVxWX1snlJE0ZbtKuLSj/adahJ29zpKAAAAAB4MFFABIA+qbNx+qHE1kmL6HcucvmqpXNnPDzRor2krOqPW/fX1NbbJRUAAAAAOCAKiADQJ73bNa/NwF579icTYvHcKfNSJ1m019TWf7jzUEV1rV1SAQAAAICjoYAIAH3i4+XZi1G+Xsp+T4LemTd90rL502Wy+w62qatv/HD7l8WllfZKBQAAAACOgwIiAPRJeGiIrUM8PRRBAb4DEQa9MzU57pklc1zk9/1ObNI1/9+uQ3ds3+MSAAAAAIYYCogA0CfjYiJdXGz7WTph9Ei5nB+/jiVxwuiXVi1yd3czb2xpbftkb/rVHLW9UgEAAACAI+AvWADoEy+lxyOJE6zvL5fLZ09JHLg86LXRI8NeeXax0tPDvNFgMO788uuM7Bx7pQIAAAAAu6OACAB9NW96cqCfj5Wd50xJDA7yH9A86LWI0JANq5f6+niZNxpNpn1HTp+6kGWvVAAAAABgXxQQAaCvlJ4e61Yu8Fb2fJpK4oTRHc/8hUMZpgrYuOYJi00qTUKkn8o4ePycyV6xAAAAAMB+KCACQD8YFhz4xrrlI8OGddXB1dXlsZkPP7N0rsVpv3BAgX4+G9c8ERoSZNF+9uK1vYdPGo1Gu6QCAAAAAHtxtXcAABgi/H29Nz6/7Hpu/sUrt27fLdHrDVJ7gK/3hNio1IcnBvh62zchrOfjpXx19dJPPz9yt7jMvP3Stdzm5pbVy+a5urrYKxsAAAAADDIKiADQb2RCxMeOio8dZTQaS8sqyisqx4+L9fRQ2DsXesNT4b5+1ePb9h+7pS4yb79x5+5f96avW/GYQuFur2wAAAAAMJhYwgwA/U8ul/t4KwP8vKkeOjU3N9d1KxdMSoi1aFcX3vto16EmbbNdUgEAAADAIKOACABAl+Ry+VOPz059OMGivaSs6oMdX9bVN9olFQAAAAAMJgqIAAB0RybEkrlTF81KsWivrK798/YvK2tq7ZIKAAAAAAYNBUQAAHo2a0risvnTLQ7Rrq1v/PPWA4X3KuyVCgAAAAAGAQVEAACsMjU57pklc1zk9/3q1Da3/GX34dsFxfZKBQAAAAADjQIiAADWSpww+oUVj7m5upo3tra2bfn86NVbanulAgAAAIABRQERAAAbjIuJfPW5Jcr7z9fWGww7D3z9XXaOvVIBAAAAwMChgAgAgG0iRoRsWL3U11tp3mg0mdKOnD79bba9UgEAAADAAKGACACAzYYFB258fllQgK95o0mIr05+m34qw2SvWAAAAAAwACggAgDQG4F+PhvXPBEaHGjRfupC1ueHTxqNRrukAgAAAIB+RwERAIBe8vFSvrp66ciwYRbtF6/lbt9/XK832CUVAAAAAPQvCogAAPSep4fiH55ZHBsdYdF+/XbBJ3vTW1rb7JIKAAAAAPoRBUQAAPrE3c113YoFE8fHWLTnFd77v12HmnTNdkkFAAAAAP2FAiIAAH3l4iJ/duncRxLHW7QXl1Z+uP3LuvpGu6QCAAAAgH7hau8A+JHJZLJp032TydSLUY5ACuy8yY1Go9Mll75anDq5TCazdxbbtCe3dxCbkbzXls2fHuDrfeT0d+aNFdW1H2z/8qWnF6kC/boaaPfkvWYymZzxh7nkwUnupC8TAAAAjoMCoqMwGo06na6qqsrWgY2Njc3NTrY+TvpTub6+vqmpyd5ZbCP9DVZXV9fY6GTziQwGgxBCo9HI5U4271hKXlNT43QFxLa2NiFEL76p7a61tdVoNDppcr1eb9/kE2LCWlqaT2Zck37QSTT1jR9s/3LFY1NDgvw7HdXS0iKc86ulra1NJpM5Y3K9Xu+kX+dGo1Gr1er1euuHmH81AgAAAL1AAdFRyOVypVIZEhJi/RCTydTU1OTj4+Pj4zNwwQaCwWAoLCz08/NTKpX2zmKb1tbWkpKSgIAADw8Pe2exjU6nKysrCwoKcnNzs3cW2zQ1NTU3N6tUKhcXF3tnsU19fX11dbVN39QOQqPRNDQ0OGPy6upqnU5n9+QLQkJCglWff3XKYDbtS9vcsufI2bXLHxsdFdZxSEVFhcFgsHvyXigrK5PL5c6YvKSkRKFQqFQqewexWWFhoZeXV0BAgPVDmIEIAACAPnKyuUgAADi+pLgxz694zM31vnfpWlvbPv38yLXcfHulAgAAAIDeoYAIAED/Gx8T+fKqRR4Kd/NGvcGwY//xzCu37JUKAAAAAHqBJcwAAAyIURGhrzy35JM96Y1aXXuj0WT6Iv2UtrllZsrE+kbt9dx8dWFptabWaDIFBfhFhQ2PGzsq0M/JNqYAMHCMRmOPW15a08dhEd5eCG8vhLcXwtsL4e2l+/C92OKGAiIAAAMlbJjqtReWfbz7q+ra+vZGkxBfnbiQnZNXVlFtMPz4m7usUnM9tyD95LeTEsYunJ3i5elke60C6HdGo7GlpaW2trb7braeq+NQdDod4e1Cp9NJR9U5HZPJRHi7GALhnXRTYJPJ1NzcTPjBN7TD9+KQPQqIAAAMoEB/341rnvh4z1dllTXm7SWllZ32N5pM313Jybtbsu6phcNUNhyUAWDokcvlnp6e3Z/209DQ4O3t7evrO2ip+lFDQ4OXlxfhB19jY6OXl5efn5+9g/QG4e3FqcM3NTV5eXn5+/vbO0hvNDU1KZVKwg++oR2+F4VR9kAEAGBg+XgrN6xeGhk2zPohNXUNf93zVUOjduBSAQAAAICVKCACADDgPD0U659ZHDsq3PohdQ1NXxw5PXCRAAAAAMBKFBABABgM7m6uj8+eIrNlSE5eYX5x2UAFAgAAAADrUEAEAGCQXM1V27pZ8eXrtwckCgAAAABYjQIiAACD5E5Bic1D8osHIgkAAAAAWI8CIgAAg6S2ocnWIXUNTSaTrdMWAQAAAKA/UUAEAGCQ6PV6W4cYjUajkQIiAAAAAHuigAgAwCDx9VLaOkSp9HBx4Zc1AAAAAHvibxIAAAZJeGiIrUMibB8CAAAAAP2LAiIAAIMkPjbK1iFenh4DEAQAAAAAbEABEQCAQRIbExk2XGXTkIvXctOOnjEYjAMUCQAAAAB6RAERAIBBIhNi+YIZbq6uNo3KyLr5p237a+sbBygVAAAAAHSPAiIAAIMnfHjwqsWzbT0XpaSs6vdb0vIK7w1QKgAAAADoBgVEAAAGVcK46FeeXRLo59NVB28vz9CQIIvGJm3zx7u/OnUha4DTAQAAAIAl21ZRAQCAvosKH/6zV1Z9l51z5Wbe3ZJyo8kkhJAJERYaPHFczJSkCa4u8qNnMi3KhUajMf1URlllzYqFM93c+A0OAAAAYJDw5wcAAHbg6uIyNTluanKcwWAsuFvYZjCMjo5ydXFp77BoVsqI4MAvjpxubdObD7x84055leb5FY91M4cRAAAAAPoRS5gBALAnFxe5j7fSz1tpXj2UPDRh9KbnlwX6+1q036uo/sOWfbcLigcrIwAAAIAHGgVEAAAcV2hI0BvrlseOCrdo1+qaP9mTfupClskusQAAAAA8SCggAgDg0JQeipdWPb5oVopMJjNvN5pM6acydn75tcUaZwAAAADoXxQQAQBwdDIhZk1JXLviMQ+Fu8VDV27m/X5LWmV1rV2CAQAAAHgQUEAEAMA5jB898vW1T4YE+Vu0V1bX/mHr/hu3C+wRCgAAAMDQRwERAACnERzo//ra5XGxURbtLS2tW9P+ln4qw2RiU0QAAAAA/YwCIgAAzkTh7vb88sc6boloEuLUhazPvjiqa2m1VzYAAAAAQxIFRAAAnIy0JeKLKxd4eigsHrqZV/jHz/aVV2nsEgwAAADAkEQBEQAApzQ2JvKNtcuHBQdatFfV1P1p6/5rt/LtkgoAAADA0EMBEQAAZxUU4Pv688sSxkVbtLe0tm3ffyz9VIaRLREBAAAA9BkFRAAAnJi7u9vqZfOWPjpNLr/vd7q0JeKnnx/RNbfYKxsAAACAoYECIgAAzk0mxPTJ8eufXeyt9LR4KFdd9Ict+8oqa+wSDAAAAMDQQAERAIChIDoi9CfrloeHBlu0V9fW/+GzfRev5dolFQAAAIAhgAIiAABDhJ+v98bVT0xOGGvRrtcb9h4+mXb0jMFotEswAAAAAE6NAiIAAEOHq6vLU4/PWrFghovc8ld8RtbNv+w63Niks0swAAAAAM6LAiIAAENNSuL4V55b4uOltGjPLyr9/Za0otIKu6QCAAAA4KQoIAIAMARFhQ//ybrlkSNCLNrrGpo+3H7wuys5dkkFAAAAwBlRQAQAYGjy9fF6dfXSlIfGWbTrDYYv0k+nHT1jMLAlIgAAAICeUUAEAGDIcnVxWbFw5qrFs11dXSweysi6+dGuQw2NWrsEAwAAAOBEKCACADDEJcfHblqzzN/X26L9bnHZ77ekFZaU2yUVAAAAAGdBAREAgKEvbLjqJ+tWxESOsGivb9R+sOPgqQtZdkkFAAAAwClQQAQA4IHgpfT4h2cenzUl0aLdaDSmn8rYc+hEm15vl2AAAAAAHBwFRAAAHhRyuXzRrJRnls51c3O1eOjS9dsfbPtSU99ol2AAAAAAHBkFRAAAHixJE0Zven5ZoJ+PRXtJedUfPk27c7fELqkAAAAAOCwKiAAAPHBGhAS9sW75mKhwi/YmXfNf96SzJSIAAAAAc5YrmByQyWQ6ceJEenp6YWGhh4fHmDFjXnjhhZEjR1p0q6ur27lzZ0ZGRm1tbVBQ0NSpU5999lmlUmnNU/RlbLvm5ub169dPnz5906ZNNg0EAGDwKT09Xlq16Ojp705fyDKZtUtbIt6rrHlq4cyOK50BAAAAPIAc/Q8Dk8m0efPmEydOCCEUCkV9fX1GRsbFixd/9atfJScnt3errq5+6623qqqqhBAeHh7l5eX79+/PzMz87//+b29v7+6fwsqx2dnZu3btUqvVKpUqNTX1qaeecnNzM7/OwYMHdTrdqlWr+vHlAwAwcOQy2aJZKWEhQZ+nn2ptu+8ElewbdyqqNC8snx/o72uveAAAAAAchKMvYT58+PCJEyc8PT1/9atf7dixY/fu3YsXLzYYDP/7v/+r1Wrbu3300UdVVVXR0dF//OMfd+/e/T//8z8hISHFxcWfffZZj09hzdiTJ0/+6le/ysvLGz16dEtLy86dO//jP/7DZPpxxkZTU1NaWtrjjz8eFBTUv58BAAAG1MTxMT9ZtyI4yN+ivbSi+g9b9t3OL7ZLKgAAAACOw6ELiC0tLbt37xZC/Mu//MvDDz/s5ubm7u7+yiuvREdH19TUXLp0SepWV1eXkZHh7u7+i1/8IiIiQiaTxcTE/PKXvxRCnD59urW1tZunsGZsa2vrhx9+OHz48A8++OA///M/P/zww0WLFmVlZZ07d679Ovv379fr9U899dTAfTYAABggwUH+b7zwZNyYKIt2bXPLJ3vTj//9ovl7ZgAAAAAeNA69hPny5ct1dXWRkZGTJk1qb5TL5UuWLDl27Ji06FgIcebMGYPBkJSUNGzYsPZu0dHR0dHRarU6MzNz2rRpXT2FNWMLCgqamprWrl0bGBgohHBxcVm3bl16evq1a9emT58uhKivrz9w4MDSpUv9/Pz6/ZMAAMAgUCjcn1/x2OkLWUdOf2deLjSaTMfPXiwpr3pmyRwPhbsdEwLm2CMbAABgMDl0ATE7O1sI0bH8N2/evHnz5rV/ePXqVSFEUlKSRbfk5GS1Wp2dnd1NAbHXY2UymcFgkP6dlpYml8uXL19uzYsCAMAxyYSYNSUxJDhwz8FvdC33zd+/eefuHz/b/8KKx0KC/IUQbXr97YKSguKyispquUwWHBwUExEaPXKEq4uLnbLjwcIe2QAAAIPMoZcwV1ZWCiGioqK676bRaIQQI0aMsGgPDQ0VQtTW1vZxbFRUlFKp3LdvX3V1tRDCYDBs2bLFZDIlJCRIVzh06NCyZct8fHysf2kAADim8TGRr69bPkwVYNFeWVP7x8/2XclRn7qQ9f/+uP2zL46e/jY7R118I6/o1IWsv+5Nf/dPO85dum40Gu0SGw8U9sgGAAAYZA5dQJSqe35+fmfOnPmXf/mXVatWvfjii++880777oeSuro6IYSXl5fFcOn9YenRrlgz1t3dfePGjWVlZZs2bfrXf/3XDRs2pKenJycnp6amCiH27t3r7u6+bNmyvr1WAAAchSrA7/UXnowfO8qivaW1beeB4+mnMnTNLR1HNWp1Xx47+8nnRyxmLwL9iz2yAQAABp9DL2GWCoiHDh06e/asEMLb21uj0WRmZmZmZi5fvvyll16Sukllvo5LUawvIPY4dvbs2f7+/rt27bpz505QUNBzzz339NNPy2SyysrKI0eOrF692voNcbRa7c2bNzu2GwwGrVZbUVFh5XXaNTQ06HQ6W0fZl/TmfF1dXWNjo72z2EaaWaPRaFycbZmetOK+urpaLnfotw06kpJXVVXJZDJ7Z7FNW1ubEKIX39R219raajQanTS5Xq93xuTNzc3C8b5a5k97KMDb8+ylm0azGVU9nqVyO7/4LzsPPr0wVS536O9ZvV7vpF/nRqOxqalJ+gljpSF2Bg57ZAMAAAw+hy4g1tfXCyHOnj07a9asF198MSgoqKWlJT09/dNPP923b19ycvJDDz0kerot7v4O2/qxiYmJiYmJFh327Nnj5eW1ZMmSHl+L+TN2FclkMvVi5VfvRjkCZ0wuBSb5YJICG41GpysgSj9enO4TLkhuJ4757TkpfrQqwPer0xdbWm0oV5WUV//94vXUSRMGLli/cMzPuZVsSj7ECojskQ0AADD4HLqAqFQqW1tb4+Lifvazn0m1A4VC8eSTT1ZWVh48eDAtLU0qIPr7++t0uqamJovh0uy2gADLXZzM9WVsWVm5KFMDAAAgAElEQVTZsWPHXn75ZQ8PDyGEVqvdunVrZmamVquNjY1du3btqFGWi7+EEF5eXlOmTOnYfvz4cS8vr+HDh3fzjBZMJlNBQYGvr6/Tbb9oMBgKCwv9/f1tPcrQ7lpbW0tKSgIDA6X/dCei0+nKyspUKpXF5u6Or6mpqaKiIiQkxOlmfdbX11dXV9v0Te0gNBpNQ0ODMyavrq7W6XTOmLyiosJgMDhm8uHDh48dE/3XvekVVRrrR2XdzJ83I8Xft4dzKuyopKREoVCoVCp7B7FZYWGhl5dX97coFpy3TtopR9sjOyUlJSgoqNM9sleuXOl0N2kAAACdcugCYkBAQG1t7dy5cy1mHs2YMePgwYMFBQXSh/7+/qWlpR2LgFJLjwXEXo/duXOnv7//woULhRAtLS1vvfVWUVFRREREYGBgVlZWVlbWb37zm3HjxlnzSgEAcFj+vt5jRoXbVEDUGwyXb9yZM8Vy5j7Qd+Z7ZEs3hEqlMjo6esmSJeZHMA/OHtnvv//+pk2bxowZU15eXlFRwR7ZAABgqHL0AmJ+fn7Hc+ukvWZqa2sNBoOLi4u/v78QorS01KJbeXm5EEJ6tCu9HltUVHTy5MmNGze6u7sLIfbt21dUVLR+/fqlS5fKZLKCgoK33377gw8+2Lx5s3WvFQAAx5VXUGLrkNy8QgqIGAhDco/surq6M2fOdGx3c3Orra3Nz8/vfnh1dXV1dbWVz+VoCG8vNTU1NTU19k7RS4S3F6cOr9FopN8gzojw9jKEw/diixuHLiBGRUVdunTp3r175u8nix+qe+Hh4dKSxoSEhPPnz2dnZy9evNi8W1ZWlhAiPj6+m6fo9dgdO3YEBwfPnz9f+vDq1asqlUqqHkrJ582bd/DgQa1W63SrdAEAsKCpa7B1SI3tQwBrDMk9sj08PMaPH9+xPS8vz9PTU3rvvCs1NTVKpdLpNleROHt4Ly8vhUJh7yC9odFoPD09nfQz79Thnf1r3nnDazQaDw8PT09PewfpDcLby9AO34stbhy6gPjoo4+mpaWlp6cvWrTIfPuzI0eOCCFGjx4tfThjxoyPP/44Ozu7rq6u/Zy7srKy3NxcpVLZ6YaD7Xo3Vq1Wnz179qc//amr64+fwE4/+0Ns1yEAwAPIaDK1tultHdWkba7S1KkCOH8W/WxI7pGtUChiYmI6tufn5ysUiu7Pca6pqfH09PT19e2mj8Ny9vAeHh5OGl76w9JJjwgnvL04dfja2lrC2wXh7aXH8L2oVsn7FmlgRURETJgwoaio6D//8z8rKiqEEFqt9tNPPz1z5oy7u/tzzz0ndfPz80tJSdHpdL/97W+1Wq0Qoq6u7t133zWZTHPmzJGWGEv27t27c+fO69evt7dYP9bcjh07RowYMWfOnPaWhISEmpqatLQ06S1rtVp9/Pjx6OjojotfAABwLnKZzMvT5ukGeoPhtx/tfv/jvcf/frFaUz8QwfBgkup3ne6RLYQw3yNb/LCrtTkr98ju9diOe2QfPnzY3d09PDw8KyvrZz/7WU5OTk8vEQAAwOE49AxEIcQbb7zx9ttvZ2Zmrl+/3tvbW3rj193d/fXXXzc/rfLVV1/Nzc3Nzs5es2ZNWFhYUVGRyWSKjIx84YUXzK+2d+/e5uZmuVweFxdn69h2ubm5GRkZP//5z80nRS5fvvzMmTNbtmw5evSoj49PXl6eTCZ77bXX+vnTAQCAPQwPCbxj+zaIQoiKKs3xqotfn70YMSJk4viYiWOjfX0sD6YAbMIe2QAAAIPPoWcgCiHCw8N/97vfLViwQKVStbS0REREzJkz53e/+5357D8hRFBQ0ObNmxctWuTv719SUhIcHLxixYr33nvPmv0HbR27bdu2yMhI6Yi9dgqF4r333lu8eLHJZCotLU1KStq8eXNsbGxfXjsAAA5iwpiovgw3CVF4r+LQ1+d/8+cdf97+5dnMaw2N2n6KhgdOVFSUEOLevXsW7R33yBZCZGdnW3Szco/s3o21Zo9stVotrXoBAABwIo4+A1EIoVKpXn/99R67+fn5bdq0adOmTd302bNnT6/HtnvnnXc6bVcqlRs2bNiwYYM1FwEAwIlMjo/95tylxiZdH69jMpnuFpfdLS479M35yLBhE8dGPzQ+xtvLKbemhr2wRzYAAMDgc/QZiAAAwO7c3d0Wz51qZWeZTDZh9Egfr+4WAUiVxINfn/t/f9wmzUls0jX3R1IMfeyRDQAAMPicYAYiAACwu6QJo8sra05eyOqx58JZKbMeechkMt0tKb+ao86+mdeo7XLqovGHOYnpJ78dExWWMC46bkyUQtF5gQaQsEc2AADAIKOACAAArLJwVoqfj9fhExf0ekOnHdzd3Z58LDU5bowQQiaTRYUPjwofvvjRqYUl5Zeu375yM6+5pbWri+sNhpt5hTfzCl1dXcaMDEsYFx0fO8rd3W2gXgycmbRH9u7duy9evFhXVxcRETF69OhVq1aFhYWZd5P2ud6xY0dGRoa0z3Vqauozzzzj6dnzqnlbx3azR/bWrVszMzOlPbJffPHFkSNH9uW1AwAA2AUFRAAAYK2pyXHjR488cf7ytVv55ouOfbyVE8dFz56S2HHlsvyHSuIT86bdzi++mqO+frugpbWtq6fQ67+vJO47+vfRI0ckjItOGBvt5sYdC+7DHtkAAACDidtxAABgA39f7+ULZix7LLWquragsFgul4+MCFMF+knnzHbD1cVl/OiR40eP1OsNtwuKr+aor90uaO26ktim10uVxC+Pn5swemTCuOjYUREuLmzfDAAAAAw2CogAAMBmcpksRBVg1LfI5fLgIH+bxrq6fl9JXK7X3ykouXT99s3bd/WGzpdFCyGaW1ovXb996fptTw/F+JjIhHHRsdERLnIqiQAAAMAgoYAIAADsw83VVaok6lpab94uuJqjzi0oNhiMXfXXNbdIlUSlh2JcTGTCuOix0RFyKokAAADAAKOACAAA7MxT4Z4cH5scH6trbrl55+7VHPWt/GKjsctKovaHSqKXp0fc2FHJcWNGhg/vYQW1EEI69LmkvKik/F5Zhbu72/AQVczIsGGqgH58LQAAAMDQQwERAAA4Ck8PhVRJ1Oqac/IKr+aob6mLjCZTV/2bdM0ZWTczsm76+XrHj4lKGBfdVSXRYDRmZOd8c+5SQ6PWrDlXCBEaHLhwVsrYmMj+fjUAAADAEEEBEQAAOBylp4dUSaxvaLp6K//KLXVhcVmXdUQh6uobz168dvbitQBf7wljohLGRUeFD29/tEnXvH3fMXVRaadjSytrPvn8yNSkCUvnTWNBNAAAANARBUQAAOC4fH28pk+Onz45vq6+8VpuQY+VRM0PlcRAP5+E8TGT4mP9fbz+sutwaUV19090/vKNljb9qsWz+zU+AAAAMBRQQAQAAE7Az9dbqiRq6hpu3L575Zb6bnFZN/1r6hpOXcg6dSHLw8O9ubnVmqe4dC13ZNiwRxLH91NkAAAAYIiggAgAAJxJgJ+PVEmsqNJcyVFn5+RVVtd209/K6qHk2JnMxAmjFe5ufY4JAAAADB0UEAEAgFMKUQXMS500L3VSeZXmao4668adKk1dH6/ZqNVdz81Pjo/tl4QAAADA0EABEQAAOLdhqoBhqZPmpU4qKq24clN95Za6rr6x11fLySukgAgAAACYo4AIAACGiIjQkIjQkMfnTrlbXHYlR301R93QpLX1ItW19QORDQAAAHBecnsHAAAA6E8yIaLChz8xb9pPXlrRi+E6W/ZMBAAAAB4EFBABAMDQ5O3pIZfJbB6l9BiIMAAAAIDzooAIAACGJrlcrgr0s3XUMFXgQIQBAAAAnBcFRAAAMGSNHxNl85DRkQMQBAAAAHBiFBABAMCQNTVpgquri/X9g4P8x8VQQAQAAADuQwERAAAMWf6+3rMfSbSys0wmW/roNLmcuyMAAADgPtwiAwCAoezR6clxsVHW9Fw48+HYUeEDHAcAAABwPhQQAQDAUCaTydYsmzdtUnz35zGPGRU2a4q1cxUBAACABwoFRAAAMMTJ5fIn5k3b9MKTsdERLl2sUC6+V9nWph/kYAAAAIBTcLV3AAAAgMEQOSLk5acX6VpaS0or7xYVt+mNpzKumn54VNfSeiVHPSkh1p4RAQAAAIfEDEQAAPAA8VS4j44KGxcdPjlhzKjIEeYPXbh8w16pAAAAAEdGAREAADygpiSON/+wqLSipKzKXmEAAAAAh0UBEQAAPKDixo7y9Vaat3ybxSREAAAAwBIFRAAA8IBykcsnJYw1b7l8446uucVeeQAAAADHxCEqAADgwfVI4vhTF7KMpu8PU2lr01++cWdacpx9U3WkaWo9c6si5159YXmtt6d7uMo3JSYoOSrQ1UVm72jAfRoaGqzvrFKpbB3iOJw6fFBQkOi/8D4+Pv1yHQCAI6OACAAAHlz+vt5joyNu5hW2t1y4dH1qcpzjlOVqta3/dyLvwMUivcH0Y+ut6m1n84f5ebw6d8zjD42QOU5cAAAADEUsYQYAAA+0R5ImmH9YUV1bUFxmrzAW8sobXvzwwhcZhfdVD39QXtf8H/uu/n9pV1r1xsHPBgAAgAcHBUQAAPBAGxsdEeh33/q7C5cd4iiV0lrd61syy2p1QnRSPWx39Erpf+y/auquCwAAANAnFBABAMADTSaTPZw43rzl2i11Y5POXnkkJpP4t73ZtU2tQggheliifOxq2f6LRYOQCgAAAA8mCogAAOBBlzJxnKuLS/uHBoMx8+otO+YRQnxzo+x6cZ31/f/vxB1dq2Hg8gAAAOBBNoAFRI1Gk5aW9uc///m7775ra2sbuCcCAADoCy+lx4TYKPOWby/fMNp1VfChyyU29a9pbD1/p2qAwvQj7g8BAACcUf+cwnzo0KF9+/apVKr/+q//klouXrz4xBNP3Lt3T/owKSnp0KFDI0aM6JenAwAA6F9TkiZcuZnX/qGmvvF2fvHY6Ai7hNEbTJn5NbaOunC7au6EYQORp3e4PwQAABgy+mEG4j/90z8tXbr0r3/9661b3y/2aW1tXbt2bfvdoRDi8uXLc+bM0ev1fX86AACAfhcdETosONC8xY5HqVQ3trTZfrByaa2d9200x/0hAADAUNLXAuLp06c3b94shHBzcxs1apTUePTo0Rs3bggh3nnnnZs3b/77v/+7ECI3NzctLa2PTwcAADBAUh4aZ/5hTl6hpr7RLkl6t5uhttVRKnHcHwIAAAwxfS0gvv/++0KIkJCQa9eu/c///I/UuG/fPiFERETEL3/5y3Hjxv36179esGCBEGLr1q19fDoAAIABMik+1t3tx91dTCbTd9k5dkkS6O3ei1FB3op+T9I73B8CAAAMMX0tIObk5AghNm3aFBsb2954/PhxIcS6detcfjjQcOnSpUIItVrdx6cDAAAYIB4K94fGjzZv+S47x2C0eSlx3/l6ug3387B1VOxwn4EI0wvcHwIAAAwxfS0gFhYWCiHGjx9v3lJUVCSEmDt3bntjWFiYEOLu3bt9fDoAAICBM3VSnPmHDU3aG7kFdkkyc7zNx6H0YsgA4f4QAABgiOlrAdHf318IodP9uGn3kSNHhBBubm6PPPJIe6NWqxVCuLv3Zj0OAADA4BgREhQeGmzeciHLPkepPDd1pJurDfdpU8eoHGcGIveHAAAAQ0xfC4jR0dFCiLNnz7a3fPzxx0KIuXPnKpXK9sbLly8LISIjI/v4dAAAAANqSuIE8w/z7t6rqNIMfoxQf8+XZkZb2VmpcH1z4bie+w0W7g8BAACGGNeeu3Tr0UcfPXv27GeffbZo0aInnnhi3759GRkZQognn3yyvU9mZuZf/vIXIcS4cQ50awsAANDRQxNivjpxQdvc0t6SkZ2z5NGpg5/kpZkxd6uajl4p7b6bm6v8P56aOFLlNTiprMH9Iazk4+Mo02YbGhrsHQEAAIfW1wLiP/7jP27evLm+vn7lypUuLi4Gg0EIMWzYsOeee04I0djYuHTp0jNnzkjtb775Zt8TD1VGo1Gn01VVVdk6sLGxsaWlped+jsRkMgkh6uvrpbVLTsRoNAoh6urqGhsb7Z3FNnq9Xgih0Wjk8r7OOx5kUvKamhqZTGbvLLZpbW0VQvTim9ruWlpajEajkyY3GAxOmtxkMjlj8ra2NplM5ozJ9Xp9N5/zcTHhl67ntX+YeSVncly0q6vLYKX70abU0NM5FbpWQ1cdgn3c314YPTawu/8F6dfuYOL+EAAAYIjpawExMDDw4MGDK1eurKqqku4C3d3d//SnP/n5+QkhmpubT548KfV84403pkyZ0senG8LkcrlCoZD2DLKSyWRqaGhQKpVeXg406cAaBoOhsbHRy8vL09PT3lls09bW1tTU5O3trVAo7J3FNs3NzTqdztfX19W1r9/1g0yr1UrJ20/tdBZScd+mb2oHUVdXZzAYnDG5RqMxGo3OmNxgMDjp57ytrU0ulztj8ubmZnd3966Sz0xJvHw9r73q1tzaVlSuSYob3WnnAXWq6+phdIj3goThTz0crnDr4cejcdAPkub+EAAAYIjph1LCzJkzr1y5kp6efvny5ZCQkBUrVsTF/XiCYVhYWFJS0tq1a59++um+P9fQJpfLbSruSBMKbB3lCKSpZC4uLk6XXPobzBmTS9U3503u6urqdAVEabKn033ChZMnl8lkzphcJpORfJDJZLJufoEODwkaFTlCXXivvSUjO+fhh+yw0vbApXvmH45Ueb2UEuTn6x0bERLkbe1bWYNfQBTcH/Yfo9EoTcbvYx9g4Az+l59Tf80T3l4Iby+Et5fuw/fi/rB/7vhDQ0Nffvnlju0qlaq4uLhfngIAAGDQTEmaYF5ALCqtKCmrChuuGswMJTXaTHWNecvqaVETgo0+Pt4BVlcP7Yj7w74zGo0tLS21tbXdd9NqtY7z543TrdJA3/X4Jdq/TCaTTqeTZjc7HcLbixTeLu+o9Z3JZGpubib84Bva4Xuxxc3gTRnQ6/UffPDBG2+8MWjPCAAA0DtxsVG+3sr6xh/36v0268aKhTMHM0NaZpHR7N5OqXCdHz+8qvxeN0OcDveH3ZPL5Z6enipVd5XrhoYGb29vX1/fQUvVvf+fvTuPi6r6/wd+ZmHf9x0VERRQNgE1rfwp5oIhKGL2MdMoM9OvH5XMzFLTyjTcytz6+CFNrVwQUzMXVD5uiAiCgCKgLDLIvswMyyy/P+7nM162YbnDDDO+no8ePbhnzr3nPTrIm/e95xzsRvISkv8RVThqKSRqSQS1g+BVhc/nGxgYqOOiK4QQPp+vr6+P4JVPs4PvQWGU6XYKJ0+e7Eq369evBwQELF68mOFwAAAAAErAYbOHD3Wnt9zLfCxsUN6uZc0iyZnUFrXCKT72+jrqMVsc+SEAAACAhmFaQIyIiDhy5IicDqWlpe++++7o0aNTU1MZjgUAAACgNIE+Q9i0/d+bm0X3Mh8rbfSLD3jV/CZ6yzR/R6WNzhDyQwAAAAANw7SAKBaL33777QMHDrR9SSQS7dy5083NLTY2lhDCYrE++OADhsMBAAAAKIepsaG7ixO95VbKg26vFtNTJ+4U0g99+pkNtDFS1uBMIT8EAAAA0DBMC4gBAQFSqXT+/Pk//fQTvf0///mPv7//kiVLamtrqW63b9/es2cPw+EAAAAAlGaErwf98HlF9ZPCEiWM+7i0Lr2wxaYEYcOdOurcByE/BAAAANAwTAuIFy9eHD16NCHko48+iomJIYTweLx33nlnzJgx9+/fJ4RYWFjs3bv31q1bAQEBzMMFAAAAUBo3FydzkxbP/d1KzVLCuK0ePzTV1x7rYaOEcRUF+SEAAACAhmFaQDQ2Nv7rr7/Gjx9PCFm+fPmsWbPc3d0PHjxICGGxWB9++OGjR4/ef/99NpvpQAAAAABKxmKxAnyG0FsyHubV84W9OqiwSXz+fovnHKf6OWhz1SmVQn4IAAAAoGEUkLcZGBicPn06JCSEEPLbb79Rc1KCgoLu3Lnz008/mZubMx8CAAAAQCUChw3mcjiyQ7FYkpz+sFdHPJf2jN8okh2yWCRUfbZPkUF+CAAAAKBJFHPjV1dX9/jx4xEREdRhZGTkjRs3/P39FXJxAAAAAFUx0Nf1dOtPb7l1L1Mi7cXNVOLuFtEPR7haOprr995wvQf5IQAAAIDGUNjMEW1t7cOHD7/zzjuEkN9//53aWQ8AAABA3QW13EqlurY+J7+oo84MpRdWPyqppbeEB6jT9imtID8E9TVnzhxjY2NjY+PNmzerOhYAAADV43ar9+PHj+V3WL16dVlZ2blz5+bPn19fXz9p0qRWHVxdXbsXIAAAAIBKuTjZ2ViZl5ZVylpu3ct0d+mVut7xltun2JjovjLIqjcGUiDkh6BCB989Puff0xV+2fr6+vPnz1Nfx8XFRUdH9+w6S5cu/de//jVo0KC7d+8qLjoAAAAV6F4BcdCgQV3vvGTJkraN0t6c8gMAAADQG4K8B8dfvCE7zM4tqKqpM2u5QTNzNYLmyw949JZp/o5sNkuxoygc8kNQlYPvHie9U0P866+/GhoajI2Na2tr09PTHz9+jDI3AAC85LD5HQAAAEAn/LzctLVe3HaVSqV37it+K5XT94qaRBLZIYfNmuqnftunACgHVT1s+7VCHD9+nBDy1ltveXl5EUJOnjzZs+sEBwdHR0e/9957igwOAABAFbr3BOLp06d7KQ4AAACAPktXR9t7iOud+9myljtp2eNe8eOwFXYvViol8XeL6S2vDba2NNJR1PV7D/JDUL62FUMFPodYW1t74cIFQkh4eLiZmVlGRsbJkyd7Not5ypQpU6ZMUUhUAAAAqtW9AmJISEgvxQEAAADQl43096QXEOv4gsxHT4YOdlHU9ZPyKgoq+PSW8EBnRV28VyE/BCXr6HlDRdUQz54929TUZGdnFxQUpK+v/+2332ZkZOTk5LSdrV9WVrZ9+/bLly/n5eUZGhq6urouXLjwzTffZLH+u/LAb7/99v7778+fP3/btm1Ui0QiiY+P379//+PHj8vLy62trd3c3KKioqZMmdLqrA0bNrz//vvffffdH3/8UVpa6ujoOHny5E8++cTExIT5ewQAAOiu7hUQAQAAAF5O9tYWjnZWRSVlspZb9zIVWEA8kVRAP3Q01/fvb66oiwP0fYUpz2p59Z12SzqYKufVA2/9HjjHpyvDDRzdT9e4/Sd8qfnLoaGhbDZ72LBhjo6ORUVFJ0+e/OSTT+jdSkpKXnvtNR6Px2KxbGxsamtrb9y4cePGjbVr1y5btqyjcdevXx8TE0MI0dPTs7S0LC0tLSoqunz58qZNmxYuXEjv2dTUFBoaeuvWLWdnZzs7u9zc3J07d6akpPz5558cDqcr7xEAAECBUEAEAAAA6JIRPh7HSq7KDnMLnj0vr7K2NGN+5fK6xus5ZfSW6YHOrL6+ewqAIuXdKChILpbfR1jd0Ol1ru+9o2eq22k3ey+bdguI1dXVly9fJoSEh4cTQlgs1uTJk/fu3RsXF9eqgPjVV1/xeLzx48fv2rXL1tZWLBYfOHBg2bJlGzZsWLhwoZ6eXtuL5+fnx8TEcLncffv2hYWFsdlsoVC4ffv2r7/+euvWrR9++CGL9m2/a9cuXV3dxMREb29vQsjFixcjIiKuX79++/btUaNGdfoGAQAAFKt7BcQffviB4Xgff/wxwysAAAAAqIS3x8CzCbcEDY2ylqS07JBxI5lfOS65UCR+sROxjhZnio8988sqB/JDUI6uVA9lPbtSQ2zXn3/+2dzcbGdnFxgYSLWEhITs3bs3IyPj0aNHbm5usp53794lhKxevdrW1pYQwuFwoqKizp07x+Pxnj59Onjw4LYXT0pKIoSEh4dPn/7fqdZ6enorV67ctWsXj8erqakxNTWVdS4vLz9//jxVPSSEjB8//s033zx58mR2djYKiAAAoHzdKyAuXryY4XhIEAEAAEBNaXG5vl6DridnyFrupj9849UALS1GUzrEEumplBYPXo33sjXW02JyTWVCfgiahJq/PG3aNPb/tkh65ZVXTExMampqTp48uXLlSllPIyMjQsi5c+e8vb25XC799I5Mnz49NDS01QTkpqYmiURCCBGLxfR2Ly+vkSNb3J9wcXEhhIhEop6+OQAAgJ5T2NaBAAAAABpvhI8HfWKxsLEpLTuX4TUTHz4vq23xaNX0ACeG1wTQPF1/qLDHjx9WVFRcuXKFEBIWFiZr1NLSeuONNwghcXFx9M4LFiwghHz33XdeXl5r1669dOlSXV2d/OtzuVxdXV0tLS2xWPz48ePjx4+vXLlyxIgRtbW1bTu33bOFrbht3wEAALqrezfMpVJp550AAAAANJSVhamLs31uwTNZy+17WcOHujO55ok7hfTDQbZGHg7qtMsq8kNQCCtXcxa784U/c67ky+8w6PUBXRlOS7+d34NOnz5NPQY4YcKEtq8+ePCAPos5IiLCxMRkw4YNqampMTExMTEx2traY8eO/fzzz2XzjtuKi4v78ccf09PTBQIBIcTCwsLPz4/H4/H5/FY96dOZAQAAVA6bqAAAAAB0Q5CvB72AWFjyvJhX7mBr2bOrFVcKkvMq6S0zAp0ZxQegnjwmunXeiZBXPwo6+G6H04Tn/Hs6kxhOnDhBCDEyMmpbvHv27JlYLG41i3nChAkTJkwoKiq6dOnStWvXzp07d/78+YsXL54/f162hCLdwYMHFy1apKenN3v27DFjxvj7+zs7O7NYLE9Pz7YFRBb2UQIAgL5ESY/Bl5eXT5s2LTo6WjnDAQAAAPQST7f+xob69JZbqZk9vtrxO4US2hN8+jrcYC/bngenVpAfQs90VCVkWD18/vz5tWvXCCF79+590EZoaCgh5OTJk1TnxsbGgoKCoqIiQoijo+PcuXN//vnnnJyct99+W8kHM1oAACAASURBVCwW79mzp90hNm/eTAg5dOjQ1q1bw8PD+/Xrx2KxJBJJp3OfAQAAVE5JBcSEhIRTp04dOHBAOcMBAAAA9BIOm91qznJq5mMhbWvmrmsWSc6mPaO3TPGx19d5WSaIID+EHmtbK2RYPSSExMfHSyQSExOT8ePHt32V2jc5MzPz4cOHhJCamhovL6+hQ4fSly80MDCgzm37OCEhRCqVFhcXE0JaTXBOSEiorq5mGDwAAEBvU0CGKhKJtm/ffuXKlZycnI465ObmEkK0tNRmP0EAAACAjgT5DLlyK1X25GBzs+jeg5xR/l7dvc6FDF41v4neMm24hmyfgvwQetucf0+XzWVmXj0k/5u/HBYWpqOj0/bV4OBgQ0PD+vr6kydPfvrpp9bW1o6OjkVFRStWrNi8ebOJiQkhJCsr6/vvvyeEBAUFtb0Ci8VydXXNysrasWPH559/rqurKxQKjx49+vnnn1MdHj161GrbZQAAgL6DaQFRLBZPnTr1r7/+6krnVatWMRwOAAAAQOVMjA3dXZyycgtkLbfuZY709+ruimUnk1tsn+LTz2ygtaEiAlQx5IegHFQNUSHVw5KSkuvXrxNCIiIi2u2gq6s7derUI0eOxMXFffrpp4SQ77//PjIy8ujRo8ePH7eysmpoaKisrCSE+Pv7f/jhh+1eZPny5VFRUTt27Ni9e7eJiUlZWRkhJDw8nM1mHzt2bOLEiZ988snq1auZvx0AAACFY1pAjI+Pp7JDBweHcePG1dXVUSuDTJ482cnJqb6+/urVq9TiIEeOHJk1axbziAEAAABUboSvB72A+Lyi+klhyQAnu65f4XFpXXphi3mL4QEa8vgh8kNQGoVUDwkh8fHxUqnU3t7+lVde6ajPjBkzjhw5kpmZmZ2dPXjw4EmTJl24cGHbtm0ZGRk8Hs/ExCQgICAyMnLOnDl6enrtXmHmzJn6+vrbt29/+PAhh8OZPHlyeHh4RERESUlJTk5OSUmJszP2UAIAgD6KaQFx//79hJB+/frdvXvXwsKCEPLuu+/GxsYGBASsXbuWECKRSJYuXbpz586DBw9GRkZiNzEAAADQAG4uTuYmRpU1L7Y+uHUvs1sFxONJLR4/NNXXHjvERmHxqRTyQ1A7CxYsWLBggfw+wcHB9BUPCSFBQUFHjhyRc0pkZGRkZCS9JSQkJCQkpFU3e3v7xMREOWdR1qxZs2bNGvlBAgAA9BKmm6g8fvyYEDJv3jwqOySETJo0iRBy8+bN/w7AZm/fvn348OFnz569dOkSw+EAAAAA+gIWixXgM4TekvEov54v7OLpwibx3+kl9JY3/R20uEra3a63IT8EAAAA0DBM81Rq+ombm5ushXrsn9qejMJisZYsWUIIiY2NZTgcAAAAQB8ROGwwl8ORHYrFkuT0h3L6051Le8ZvFMkOWSzypp+jguNTHeSHAAAAABqGaQGRy209Cdre3l5XV7egoIDP58sahw0bRgihP5kPAAAAoNYM9HU93frTW27dy5RtzSxf3N0i+uEIV0tHc30FxqZayA8BAAAANAzTAqKjoyMhJCcn58UV2eyBAwdKpdLU1FRZo5mZGSGEx+MxHA4AAACg7xjh60E/rK6tz8kr7KizzP2C6kclLVZSmx6gUTsnID8EAAAA0DBMC4geHh6EkH379lVUVMga3d3dCSHnzp2TteTm5hJCrKysGA4HAAAA0HcMcLKzsTKnt9xKzer0rBPJLYqMNia6owZZKjgylUJ+CAAAAKBhmBYQqcVrioqK/Pz8vvjiC6px3LhxhJBdu3Y9ePCAECIQCDZu3EgIGTRoEMPhAAAAAPqUIO/B9MPs3IIq2tbMbdUImi8/aPHM3TR/RzZbo7YhRn4IAAAAoGGYFhBHjx49ffp0QkhBQcFXX31FNUZGRpqYmFRVVXl7e3t4eNjZ2VH76y1YsIDhcAAAAAB9ip+Xm7bWiyX/pFLpnbRsOf1P3ytqEklkhxw2a6oGbZ9CQX4IAAAAoGGYFhBZLNbhw4c3b948atQoHR0dqtHCwmLv3r0cDkcsFmdlZdXW1hJCZs6cGRERwTReAAAAgL5EV0fb28OV3nLn/kOxWNJuZ6mUnGq5fcrrQ2wsjXR6MT5VQH4IAAAAoGFa75HXA9ra2itWrFixYkVTU5OscebMmW5ubnv27MnNzXV2dh43btysWbNYLI2angMAAABACBnp50l/6rCOL3iQ82TYYJe2PZPyKgorBPSWsACnXo9PFZAfAgAAAGgSBRQQZbS1temHPj4+P/30kwKvDwAAANAH2VtbONpZFZWUyVpu38tst4B4IqmAfuhoru/f37xtN02C/BAAAABAAzCdwrxly5YtW7ZIJO3P05EpKCjYsmVLbGwsw+EAAAAA+qARvh70w9yCZ6XlVa36lNc1Xs8po7fMCHTWyMfvkB8CAAAAaBimBcTo6Ojo6OhOE8Ty8vLo6OjVq1czHA4AAACgD/IeMlBft8VShklpWa36xCUXisRS2aGOFmeyj70yglM65IcAAAAAGoZpAbErxGLx1atXCSFlZWWddgYAAABQO1pcrp+XG70lJf1Rc7NIdiiWSE+lFNM7BHvZGutpKSm+vgf5IQAAAIAa6ckaiJaWlq1abGxs5CyALRQKBQIBIcTBwaEHwwEAAAD0fUE+Q64np8ueMBQ2NqVl5w4f6k4dJj58XlbbQO8frlnbpyA/BAAAANBgPSkgVlRUtGqprKzsyokff/xxD4YDAAAA6PusLExdnO1zC57JWm7dy5QVEI8nFdI7u9kaeTiYKDW+Xob8EAAAAECD9aSA+OWXX8q+XrduHSFkzZo1bLa82dBaWlqBgYHBwcE9GA4AAABALYzw9aAXEItKyop55Q62lsWVgrv5LappMwKdlR5d70J+CAAAAKDBelJAXLt2rexrKkH84osvuNyeXAoAAABAY3i69Tc21K+tF8habqVmTp/46vE7hRLpi+1T9HW4471sVRFgL0J+CAAAAKDBmGZ1CxYsIITIWeCGIaFQyOfz233JzMyMw+HIDmtqao4cOZKUlFRdXW1hYTFy5MhZs2bp6+t3ZRQm58o0NDRERUW98sorCxcu7NaJAAAAoBnYbPbwoe6Xb96TtaRmPg4eHXA27Rm92xQfe30dTa6s9XZ+CAAAAABKxjR53b17t0Li6MiJEyd+++23dl/auXNnv379qK8rKiqio6PLy8sJIbq6uqWlpXFxccnJyd99952hoaH8Ibp4blpa2tGjR/Py8iwtLUePHj1jxgwtrRY7J54+fVooFM6cOZPJ+wUAAAC1FuQz5MqtVNnzhs3NogMX06v5TfQ+04Zr1PYpbfV2fogbzAAAAABK1tfvfj979qzzToTs3bu3vLzcxcVl+fLljo6OeXl533zzTVFR0S+//PLRRx8xP/fKlSsxMTG6urqDBg0qLS09cuRIVlbWunXrZLfW+Xz+iRMnJk+ebGFhweT9AgAAgFozMTZ0H+ic9fiprOVCZjkhL0pavv3MBlp3cncT5MMNZgAAAAAlk7eydV9QUlJCCNm5c2d8G7LssKamJikpSVtbe9WqVU5OTiwWa+DAgZ999hkh5Nq1a01NTXKu35Vzm5qa9uzZY2tru3v37o0bN+7Zs2fSpEmpqak3btyQXScuLk4kEs2YMaP3/igAAABALYzwGSL7mi/RKm/k0F8NC9Dwxw+VoLs3mH/88cfffvtt69at1tbW1E1ihZx75cqVNWvW5Obmurq6NjY2Hjly5KuvvpLSFrvEDWYAAADQGH26gCiVSktKSlgslr29vZxuiYmJYrF42LBhNjY2skYXFxcXFxeBQJCcnMzw3CdPnvD5/LCwMHNzc0IIh8OZO3cuISQjI4PqX1tbe+rUqalTp5qYmDB4uwAAAKAJ3FyczE2MqK+LRcb0l0z1tccOsWnvJOgG3GAGAAAAULI+XUCsr6/n8/m2tratJoO0kp6eTgjx9fVt1e7n50cISUtL641zWSyWWCymvj5x4gSbzQ4LC5MzEAAAALwkWCxWoM8QQohYynouarFk3pv+DlrcPp199X24wQwAAACgfH06haVuLzs6Ov7nP//56quvFi1atHbt2tjY2MrKSnq3qqoqQkjbJNLOzo4QUl1dLWeIrpzbv39/fX39kydPVlRUEELEYnFsbKxUKh06dCh1hT///DM0NNTIyIjRuwUAAABNEeA9mMvhlIoMRLRci81iTfPH/GWmcIMZAAAAQPn69CYq1AI3KSkpd+7coVoKCwtTUlLOnz+/ZMmSESNGUI01NTWEEAMDg1anU0tcU692pCvnamtrf/jhhzExMQsXLqQ2UXn+/Lmfn9/o0aMJIX/88Ye2tnZoaGgX35RYLK6vr2/bLpVKxWJxY2NjF69DnUIIEYlE3TqrL5BIJISQ5uZmtYu8ubmZ+r9s/xx1QUXe1NRE/eGrEVnkbHafvuHRlkgkIoSo3YecECIWi6VSKSJXJolEIpFI1DRyop6f8x782O0WLps1xNX5VloDvXG4i5mFPpvhoD2IXO3+5ZePfoM5ISGBx+NZWVkNGDBg6tSp1MOAFKXdYA4MDLSwsGj3BvP06dNxg7m3zf1rTuzEg6qOAgAAQPP16QIilSCKxeKwsLCxY8daW1vn5+f/8ssvWVlZMTExP/30E7UiNVXma7ubXtcLiJ2e+/rrr5uamh49evTx48cWFhZvvfVWREQEi8UqKyv766+/Zs+era+vT7qmvr4+MTGxbbuWllZdXV0XFwWnq66ulp8E91mtniRVI9SejOro+fPnqg6hh3g8nqpD6KEefFP3EYhc+dQ3cqFQqOoQeqK5uVkgEPTiALom9S0rd54mzQr5W66rq6urq+t6f/q2HhpAI28wAxOoIQIAAChB9wqIa9eura6ujo6OdnBwoFqWLl1KCPn+++85HI7cU3vCzMxszJgx3t7eEyZMoFo8PT2//vrrpUuXPn369MiRIx9//DHpLC2mHl/qSNfP9fHx8fHxadXh999/NzAwCAkJkf9G6AwNDceMGdO2PSkpycjISP5qPq1QawCZmpp2vXzZR0gkEh6PZ25urqurq+pYuqe5ubmsrMzS0lJbW1vVsXRPY2NjRUWFtbU1l9unbxu0JRQKq6qqbG1t1e4JRD6fX1NT061v6j6irq6Omh6o6kC6raamprGx0draWtWBdFtVVZVYLLa0tFR1IN1WUVHBZrPNzMxUHUi3lZWVaWlpmZqa9t4QWXda3N7TZYm0Gqrt7f0ZXpbH4xkYGHTruTaJRJKZmclwXDmUnB9q5A3m2tramzdvtm2XSqU1NTVPnz6Vf3plZSX11GRfQH8OtLfN/WuO0sYCOTr9iCqWVCqtqqpS00coELyqSKXS6upq+f/491kSiQTBq4RmB9+DG8zdKyVs2rSpoaEhJCREliBu376dELJly5buDtwVEydOnDhxYqtGDoczffr0mJiYhw8fUi2mpqZCoZDP57fqSc0Ulv9bDZNzeTzehQsX5s+fT1XBBALBwYMHk5OTBQKBm5vbO++8M2DAgLZncTicdtfSZrFYHA5HR0dHzoitUH/fXC63W2f1BdTyQFpaWmoXOTVzWR0jp+avaWtry18xqg+iJgJra2v3xm+hvYqaYKh2HxVCiEAgYLFY6hg5h8NR08jZbLZUKlXTyNlstjpG3oMfu91SI2i+9rDF4+p23Pqcp7WChiYzE0ZzWnsQeW9PYVZyfqiRN5h1dHRcXV3btufm5urq6sqvdFdWVurp6andTVnFUshDiHl5eW3/Ngkhjo6O7u7uUVFRU6ZMobf//PPP//znPxcvXrxx40aGQ7cSFRX1+++/Hz58uFufIhXq1ZsxbVVVVanvZ16tv2HVOviqqipdXV09PT1VB9ITCF5VNDv4HuSH3SsgmpmZlZSU/Pzzz4GBgfR7qtRv+F0aTxFPP1GFuaKiIrFYzOFwTE1NS0pK2hYBqZZOC4g9PvfIkSOmpqZUibOxsTE6OrqwsNDJycnc3Dw1NTU1NfWbb74ZPHhw998fAAAAqLfT94qaRC/SMhaR2nHrpVLpnbTsCa8GqDCw3qDk/FAjbzDr6OgMHDiwbXt+fr6Ojo78fZypX+mNjY3l9FGmbs2vZ6LV44cKnMjMZrNlq12LxeKioqKioqJLly7NnTt3586dChlCwyh5q3Hqt2I13d8cwatKdXU1glcJBK8qnQbfgwJi96YEjh07lhBy9OhRExMTLS0t2aNMenp6Wl3T3fjaRd1119XVpaY0Ure8qPksdKWlpaSzG2I9PrewsPDKlSuRkZHUVNaTJ08WFhZGRUX98MMPmzZt2rp1q7a29u7du7v/5gAAAEC9SaXk1N0ieosVR6jNEhNC7tx/KBZr1JYmpM/kh/QbzOR/WVyPbzD3+Ny2N5jPnDmjra3t6OiYmpq6bNmy7Ozsbr4zaEevTl7m8XhV/1NWVpacnPzBBx8QQmJjY//888/eGxcAAKAv614B8dtvv/Xw8OilUFppaGj4+OOPP/7447YLnBcVFRFCnJycqHuD1G53aWlprbqlpqYSQry8vOSM0uNzDx8+bGVlFRwcTB2mp6dbWlpOnTqVCql///7jx4/Py8vr3dXZAQAAoO+5nVteWNEiAbDn/veZrDq+4EHOExXE1JuUmR/KgRvM0BtVRR0dHTc3ty1btlBb4hw/flzhQwAAAKiF7k0odnJyunv3bmJi4uPHj6m7u4sXLyaEbNu2TeHLk+nq6pqZmaWlpf3999/Tpk2jv0Td+vP29qYOx4wZ8/PPP6elpdXU1Miez+TxeI8ePdLX15dtxteunp2bl5d3/fr1JUuW0GfctPv8Z2+vOgQAAAB9zck7hfRDE22pKbtBdnj7XuawwS5KD6oXKTM/bGhoWLFiBSHku+++a7VFSdsbzDdv3kxLS2u1bl0XbzD37Nyu3GA+ffq0QCBQux3w+hSV7J0yfvz4U6dOZWVlKX9oAACAvqDbKxLq6uoGBwfLEiMqQVy0aFFvbO06adKktLS0X375xdDQcOzYsRwOp6qq6tChQykpKRYWFtOnT6e6mZiYBAYG3rx5c8uWLatWrdLX16+pqfn222+lUunYsWPpu+X+8ccfIpFo2LBhnp6e3T2X7vDhw/b29tSEHcrQoUPT09NPnDgRHh7OYrHy8vIuXrzo4uLSdv8+AAAA0GDldY3Xc8roLeOHWFTkFMgOcwuelZZX2Viq39bVcigtP8QNZpBPgSshtkJtrWNgYCCnj0QiiY+P379//+PHj8vLy62trd3c3KjdV2SLKlISEhIOHjyYkpJSWlrq6uo6bdq0RYsWydmboqSkZOLEifn5+R999NE333zT6moAAABKwDSr++abbwgh1FQRhRs1atTUqVNPnz69Y8eOH3/8UV9fn1qV2dzcfMWKFfQfsR988MGjR4/S0tLefvttBweHwsJCqVTq7Ow8Z06L+5N//PFHQ0MDm82WFRC7fq7Mo0ePkpKSVqxYQb+pHhYWlpiYGBsbe/78eSMjo9zcXBaL9dFHHyn4TwQAAAD6tpPJhSLxiz18dbQ484O9fizMFDQ0yhqT0rKmjhuliuiUpFfzQ9xg1lQPKjLKBGWddttzv5M54FcKE7oy3HDbAEOtbvxFXLhwgRDy2muvyemzfv36mJgYQoienp6lpWVpaWlRUdHly5c3bdq0cOFCWbeYmJi1a9cSQrhcrrGxcVpaWlpaWnx8/IULF9r9dFVUVISGhubn57/77ruoHgIAgKowLSB++umnComjI1FRUd7e3vHx8cXFxXw+383NbfDgwZGRkUZGRvRuFhYW27ZtO3z4cFJSUnFxsZWV1ejRoyMjI7uy33Z3zz106JCzs/Po0aPpjTo6Ops3b6Z22SspKfH19X333Xf79evH5L0DAACAehFLpPEpxfSWYC9bCyM9Py+3/ySnyxpT0h+98WqgtpbiZ2/0Eb2aH+IGs6ZKKLx8h3dHfp/qxupOr7M1JcZUR94alxQX04FdKSAKhcInT57s27fv9OnTTk5OixYt6qhnfn5+TEwMl8vdt29fWFgYm80WCoXbt2//+uuvt27d+uGHH1KFv6ysrHXr1nG53M2bN8+ePVtPT+/+/fvvvPPOvXv3YmJi2n7v1NbWhoWFZWdnR0REbN26FdVDAABQFYVlrjU1Nb/++mtCQkJ6enpVVZVIJDI3N/f09Bw3btycOXPkr1QtB4vFCgwMDAwM7LSniYnJwoUL6Tf32vr99997fK7M+vXr223X19dfsGDBggULunIRAAAA0DzXsp+X1TbQW8IDnAghQb5Drieny55LFDY23c/KHT7MXekBKlsv5Ye4wQzyVTdWd6WG2BFra+u2jQMHDvzzzz8tLCw6OispKYkQEh4eLnsMVk9Pb+XKlbt27eLxeDU1NdQHfsOGDVKpdOXKle+99x7VbdiwYT/88MPkyZPPnDnTqoAoFApnzpyZmpo6ZcqU3bt3K3xRUQAAgK5TQAFRKpXu27dv+fLl9fX19PbKysrHjx+fOnXqs88+i4mJiYqKwh0zAAAA0GAnWm6f4mZr5OFgQgixMjd1cbbPLXgme+lWaqZmFxB7NT/EDeaXU1ceP1QIbW1t+seyublZIpHk5uZGRUXt37/fwcGh3bOmT58eGhraqsbX1NRELXlJ7S8kkUguXLjAZrOjoqLo3UaOHLly5UoWiyWRSGRz/xsbG2fPnn3jxg0zM7MDBw5oaWkp9m0CAAB0iwIKiN9+++1nn31GfW1sbOzm5ta/f38ul5ufn5+dnV1TU1NfX//BBx/U1tYuX76c+XAAAAAAfVBxpeBufiW9ZUags+zrEb4e9AJiUUlZEa/M0dZKefEpF/JDUC0mDyEWFRXR58I3NTVlZWWtXr362rVrkyZNun37drsPonK5XGr/HLFYnJ+fn5aWlpSU9Pfff9fW1sr6lJSUNDQ02Nvbt3qSkcPhrF69utUF16xZQ+0tXlVVFRcXN2vWrJ69HQAAAIVgWkBMT0///PPPCSEWFhZffPFFVFSUvr6+7FWhULhv377169dXVFSsXLkyJCTE3V2Tb7YDAADAS+vYnUKJ9MX2KYa63OChdrJDT7f+xob6tfUCWcvte1mOkzSzgIj8EHrA0dBJYCHo6NUk3m0tdvd+c/G08JTzqg5Hp4vX0dbW9vb2Pnr0qI+Pz5MnT44dO9bRUphxcXE//vhjenq6QCAghFhYWPj5+fF4PD6fT3UoLCwkHUyRbquoqGjatGmenp4bN25cs2bNlClTWk3SBwAAUCamBcQffvhBIpFoaWnFx8ePGtV6P0E9Pb0lS5b4+fmNHTtWJBJRa10zHBEAAACgr2kWSc6lPaO3TPK219N+MZmRzWYPHzb48o0UWUtq5uPJY4P0dLtaxVAjyA+hB6a5hsl5de5f7dfs5HhQ8SB24kEGEbVgaGj46quvHjt2LCMjo90OBw8eXLRokZ6e3uzZs8eMGePv7+/s7MxisTw9PWUFRCsrK0JIZWVlu1doJTg4eP/+/RKJ5PDhw/n5+Zs2bdqwYYOi3g4AAEB3sRmen5CQQAiZM2dO2+xQZvTo0XPnziWEXLx4keFwAAAAAH3QhQxeNb+J3jJtuFOrPkHeg9n0hdVEonsPcpQRnNIhPwTF6kH1sDdQCyPSZzfTbd68mRBy6NChrVu3hoeH9+vXj1rTkNolnOLs7MzhcIqLi1utDUoIWb9+/dKlS6uqqmQt8+bN09bW1tXV3bRpEyFk165dOTma+S8GAACoBaYFxOLiYkJIUFCQ/G4jRowghFCreAAAAABomFbbp/j2Mxtobdiqj4mxoftAZ3rLrXuZUqKBkB+CAjGpHiqw8lhfX3/t2jVCiKdnOzOjpVIp9bH39vamtyckJFRXv9j7RUtLKygoSCwW79u3j94tLy9vy5Yt8fHx7W5NPnHixAkTJohEok8++UQq1ch/MwAAQA0wncJM/Qzr9CcZ9l8GAAAATZXDq8soarE/bHhA68cPKSN8PbIeP5UdPq+ozi8scXGya7ez+kJ+CAqkwGnIPdPY2JiZmfn555+Xlpba29tPmjSpbR8Wi+Xq6pqVlbVjx47PP/9cV1dXKBQePXqUWgyUEPLo0aORI0cSQr788ss33nhj48aN5ubmkZGRurq6BQUF1KbMM2bM6OibYtOmTQkJCZcuXTp79uyUKVN67b0CAAB0iOkTiA4ODoSQ27dvy+9269YtQoijoyPD4QAAAAD6mlaPH5rqa78+xKbdnm4uThZmxvSW2/cyezEyFUF+CGrN1tbWjMbKyuq1115LTEzU19f/8ccfO9rJhNpPfMeOHY6OjgMHDrSxsfm///u/4ODgGTNmEEImTpy4ceNGQsjIkSNXrVrV3Ny8ePFie3t7V1dXLy+v5ORkLy+vdevWdRTSwIEDFy9eTAj59NNPGxoaeuVtAwAAyMW0gDhu3DhCyMGDB6lH+tt1/fr12NhYQsj/+3//j+FwAAAAAH2KoFH0d3oJveVNfwctbvspFouQgGGD6S0Zj/Lr+cJejE8VkB+CWpNIJGIaDofj7u7+1ltvJSUlUZ/tds2cOfPw4cNBQUH6+vocDmfy5Mn79+8/cODAhg0bvL29raysnJ3/u4LBqlWr4uLipk6dam9vLxAIfH19v/jii4SEBD09PTlRRUdH29nZPX36dPv27Qp+wwAAAF3AdArzkiVL9u3bJxKJpk2btmbNmg8++MDAwED2qkAg2Ldv31dffdXc3Mxms6n7ZgAAAAAa41zaM36jSHbIZrGm+bc/f5kS4D344n/uisRi6lAsltxJfzh2hE/vRqlcyA9BHbm4uNTW1na9/3vvvffee+/RW0JCQkJCQlp1s7e3T0xMbNU4duzYsWPHdnTl/fv379+/v1WjgYHBw4cPux4eAACAYjEtIHp4eGzatCk6OrqqqmrZsmVffvmlu7t7v379CCFPnz59+PChbN+xb77581SRoAAAIABJREFUxsPDg2m8AAAAAH3JieQWe4CMcLW0N5P3GJGBnq6n+4C0zMeyltv3Ml8L8mZr0IKAyA8BAAAANAzTAiIhZMWKFRYWFv/3f/9XV1dXV1eXnJycnJxM72BkZBQTE9PqBh0AAACAuksrqMotraO3dLR9Ct0InyH0AmJ1bf2jvMLBLTdoVnfIDwEAAAA0iQIKiISQefPmTZ8+/ddff718+XJGRkZVVRUhxMzMzMvLa+zYsW+//baJiYlCBgIAAADoO1ptn2JjojtqkGWnZw1wsrOxMi8tq5S13L6XqWEFRIL8EAAAAECDKKaASAgxNjZeuHDhwoULFXVBAAAAgL6sWtCUkFlKbwkb7sRmd2kmcpDPkPgL12WH2XmFVTV1Zibt7+6qvpAfAgAAAGgGprswAwAAALycTqcUN4kkskMuhxXi69DFc/293HS0tWSHUqk0KS1bwfEBAAAAACgICogAAAAA3SaVkviUFtunvDbExtJIp4un62hreQ8ZSG+5cz9bLJZ01B8AAAAAQIVQQAQAAADottu55YUVAnpL+PDOt0+hG+HnST+s5wsf5DxhHhgAAAAAgMKhgAgAAADQba22T+lnaeDX37xbV7C3tnCys6a33LqXqYDIAAAAAAAUDQVEAAAAgO55Xttw/VEZvSU8wInVpd1TWhjh60E/zCt4VlpexTA2AAAAAACFQwERAAAAoHtO3S0SS6SyQx0tziRv+x5cZ9gQF33dFssmJqVlMQ0OAAAAAEDRUEAEAAAA6AaxRBqfUkxvCfayNdbT6qi/HFpcrp+XG70lJf1RU7OIUXwAAAAAAIqGAiIAAABAN1zLfl5W20BvCQ/o3vYpdCP9POlTn4WNTfezcnt8NQAAAACA3tArBUSRSCSRSHrjygAAAACq1Wr7FDdbIw8Hkx5fzcLM2KVfi+nPt1I1cysV5IcAAAAA6kthBcS///577ty5AwYMMDAw0NLSevbsGSHk6tWru3fvrqioUNQoAAAAACpUXCm4m19Jb5kR6MzwmiN8WmylUlRSVsQr66izekF+CAAAAKAZFFBAbGxsfPvtt994441ffvnlyZMnAoFA9lJ+fv7ChQvt7e1//vln5gMBAAAAqNaxO4US6YvtUwx1ucFD7Rhe09Otv7GhPr3l9j2130oF+SEAAACAJmFaQJRKpQsXLjx8+DAhxNzcPCwsjP6qmZkZi8VqamqKiorasGEDw7EAAAAAVKixWXw2tcX2KZO87fW0OQwvy2azhw8bTG9JzXwsbGhkeFkVQn4IAAAAoGGYFhBv3Lhx4MABQsjEiRNzcnJOnDhBfzU0NDQ7O3vkyJGEkPXr1+fl5TEcDgAAAEBVLj4orRE001umDe/59il0Qd6D2awXm6k0i0QpD3IUcmWVQH4IAAAAoGGYFhB3795NCLG2tj569Ki5uXnbDm5ubhcuXHB2dm5ubv7uu+8YDgcAAACgKidbbp/i289soLWhQq5sYmw4eGCLtRRv38uUdtS7z0N+CAAAAKBhmBYQ79y5QwiZN2+eiUmH+w8aGBgsWLCAEHLv3j2GwwEAAACoRA6vLqOomt4SHqCYxw8pQb4ttlJ5XlGdX1iiwOsrE/JDAAAAAA3DtID49OlTQoiXl5f8bm5uboSQ7OxshsMBAAAAqMTxOwX0Q1MD7deH2Cjw+m4uThZmxvSWW/cyFXh9ZUJ+CAAAAKBhmBYQdXR0CCF8Pl9+t5ISdb2FDgAAACBoFP2dzqO3hPo5anGZ5lF0LEICW26l8uBhfl29oKP+fRnyQwAAAAANwzTxdXV1JYQkJyfL73b16lVCyIABAxgOBwAAAKB859KeCRpFskM2ixXq76jwUYZ7D+ZyXuzpLJZIkjMeKXwUJUB+CAAAAKBhmBYQQ0NDCSGxsbE5OR3uFXj8+PHjx48TQiZNmsRwOAAAAADlO5FcRD8c4Wppb6an8FEM9HQ93VtU027fy5RI1W8zFeSHAAAAABqGaQFx8eLFlpaWzc3Nr7/++qVLlyQSCf3VoqKiRYsWzZo1ixCir6+/ZMkShsMBAAAAKFlaQVVuaR29JTxQkdun0I1ouZVKdW39o7zCjjr3WcgPAQAAADQMl+H5pqamJ06ceOONN549ezZ+/HhLS0uqPSQkpKCgoKqqijpksVixsbF2dnYMhwMAAABQshN3WpTwbEx0R7la9tJYAxxtbazMS8sqZS2372UOHujcS8P1EuSHAAAAABpGAYt/jxkzJikpKTAwkBBSXl5ONaalpcmyQxcXl0uXLs2YMYP5WAAAAADKVC1oSsgspbeEDXdis1m9N2KQzxD6YXZeYVVNXUed+yzkhwAAAACahOkTiBQvL69bt27dvHnz7NmzKSkp5eXlIpHI3Nzc09MzODh40qRJHNqK4AAAAADq4nRKcZPoxQxcLocV4uvQqyP6e7mdv5rU2NRMHUql0qS07DdeDejVQXsD8kMAAAAAjaGYAiIhhMVijRo1atSoUYq6IAAAAIBqSaUkPqXF9imvDbGxNNLp1UF1tLW8hwxMSsuWtSSlZY17xY+rhuU25IegRvh8PjWhPiMjw9m5nXUDampqnJycCCFZWVkODr17IwEAAKCvUVgBEQAAAEDD3M4tL6wQ0FvCh/fW9il0I/086QVEvqAh89GTYUMGKmFogD5rypYr3ep/ZsXrvRIHzdKlS//1r38NGjTo7t27vT0WE+oSJwAA9GVMC4hHjx7tSjcjIyMbGxtbW1tra2ttbW2GgwIAAAAoQavtU/pZGvj1N1fCuHbWFk521oUlz2Utt1Kz1KiAiPwQAAAAQMMwLSC+9dZb3T3FxcUlPDx82bJl2HQPAAAA+qzntQ3XH5XRW8IDnFi9uHtKCyN8PegFxLyCZ6XlVTaWZkoanhnkh/CSCA4OtrCwsLCwUHUgnVCXOAEAoC9jugsz9aPI0NBQfjcjIyN9fX3q67y8vC1btri7u//1118MRwcAAADoJXHJRWKJVHaoo8WZ5G2vtNGHDXHR122x2GJSapbSRmcI+SG8JKZMmbJmzZqPPvpI1YF0Ql3iBACAvoxpAbG8vDwxMZG6V2xubv7JJ58cOXLk+vXrt2/f/v3337/88ktbW1tCiIeHR2FhYUVFRUpKyrJly/T09Orq6sLDw4uKijobAQAAAEDZxBLp6XvF9JYJQ22N9bSUFoAWl+s31I3ecjfjUVOzSGkBMIH8EF4Sv/32m7Gx8dKlS2UtZWVln3/++ahRo2xtbV1dXSdOnHjq1Cmp9MWtiIiICGNj44qKips3b06bNs3R0dHd3f2tt946duwYvRshRCKRxMXFhYSEDB482NLS0sPDY9q0aX/++Se9GxXAjh07hELhunXrvLy8rKysfH19V69eXVNTIydOQkhCQsL8+fN9fHzs7OzGjBnz/fffNzQ0KP7PCAAANAXTKczl5eUTJkwoKir6xz/+sWfPHtltZEJIYGBgRETEqlWrli5dunv37jfffDMhIcHX19fX1/f999/38/MTCoXbtm3bsmULwxgAAAAAFOta9vOy2ha/S4cpZfsUupG+ntfvpMtKBQ2NTfezcq1N9ZQcRg8gP4QeyH5WW1Ynr4DVJBJ364KJD5/LedWvv7mBjoL3kywpKXnttdd4PB6LxbKxsamtrb1x48aNGzfWrl27bNkyes9Tp04tX75cLBbr6+uXlJScOXPmzJkzt27don/y169fHxMTQwjR09OztLQsLS0tKiq6fPnypk2bFi5cSL9aU1NTaGjorVu3nJ2d7ezscnNzd+7cmZKS8ueff3I62L09JiZm7dq1hBAul2tsbJyWlpaWlhYfH3/hwgUsSAoAAO1i+lPz22+/LSoqGj58+M8//9zuDxsdHZ2dO3fev3//+vXrP/74I3Xja/DgwWvWrPnss88uXbrEMAAAAAAAhWu1fYqbrZGHg4mSY7AwM3bpZ5/79JmsJfHO/Vd83YVNYm1dPQM9XSXH03XID6EHTiQXtlp1tJX6xu4VEL89nSnn1Z3vDHex7mSWfXd99dVXPB5v/Pjxu3btsrW1FYvFBw4cWLZs2YYNGxYuXKin96L6Hx0d7ebmtnfv3qFDh1ZWVv7www8xMTF79+6dMWPGiBEjCCH5+fkxMTFcLnffvn1hYWFsNlsoFG7fvv3rr7/eunXrhx9+yKItyLpr1y5dXd3ExERvb29CyMWLFyMiIqhnfkeNGtU2zqysrHXr1nG53M2bN8+ePVtPT+/+/fvvvPPOvXv3YmJiPv30U8X+sQAAgGZgWkA8ceIEISQ0NFTOrSoulxsaGnrjxo1///vfsifnx4wZQwh58uQJwwA0iUQiEYm6MTWJmr/Q3bP6ArFYTP0fkSuNLHKW0tb/VxAqcpFI1GpeT98nkUgIIWr3USFqHrlUKlXHyKVSKSJXMqlUKucHaHGVMDm/gt4SHuCokrcZMMydXkAsLa86ceEW9bW9tYWPh2ug92Aut/0njGSob2plQn6oQF3J9NQxG+zLhg0b1m6+1GkqcvfuXULI6tWrqUn6HA4nKirq3LlzPB7v6dOngwcPlvXU0tI6e/YstauJpaXl2rVr6+vr9+7d+80335w6dYoQkpSURAgJDw+fPn06dYqent7KlSt37drF4/FqampMTU1lVysvLz9//jxVPSSEjB8//s033zx58mR2dna7BcQNGzZIpdKVK1e+9957srf8ww8/TJ48+cyZMz0oICr/46fWn3kEryoIXlUQvKrID74H+SHTAmJxcTEhpH///vK7OTs7E0JycnJkLf369SOE8Pl8hgFoDIlE0tjYWF1d3d0TBQJBc3Nzb4TUe6j0i8/nNzY2qjqW7qG+x+rr64VCoapj6R7qH47a2lo2m+nKp0omi1ztSp9NTU2EkB58U6tcY2OjRCJRx8ibmprUN3KpVKqOkYtEIhaLpY6Ri8XipqamjiI/cr2IXigw0OEMd9BVydu0szTR1uK2u/Ths+cVz55XXE9OD/l/gXZW5nIuovwbMMgPFaWL+aFAIOg7v97o6Oh03qlv63HN3cjIiBBy7tw5b29vLve/v2cdP368bc85c+a02hN56dKle/fuvXbtmkgk4nK506dPDw0NbTUBmfoxR/53e1XGy8tr5MiR9BYXFxfSQV1PIpFcuHCBzWZHRUXR20eOHLly5UoWiyWRSLqbLir5n0epVCoUClv9IagLBK8qVPDKv6OmEFKptKGhAcErn2YH34P8kGkB0dTU9Pnz56mpqf/4xz/kdEtJSSGE0J/bf/78OSHEwcGBYQAag81mU+ubdP0UqVRaV1dnaGhIJStqRCwW19fXGxsb0xdFUgtNTU18Pt/ExERXt+9OHGuXUCgUCoVmZmZaWsrbAUAh+Hy+UCg0NzfvaBGfPqu2traxsbFb39R9RFVVVV1dnTpGXlFRIRQK1THy58+fi8VidYycx+Ox2Wx1jLyxsVFHR6fdyBubxQkP79NbJvs4ONpZKyu0F6SEHDtzRf7GKTX1gt/OJr4TNsHNpcMlGpWf+CI/VJSu5IdUNmhsbKy0qOSrq6tTdQhMZWRkUNXtVmpqapyc5K2FumDBgqSkpO++++7QoUOzZs0aM2ZMYGBgu4m6r69vqxZHR0crK6uysrLi4uJ+/fpxuVyqBCkWi/Pz89PS0pKSkv7+++/a2tq2Vxs0aFCrFjkVwJKSkoaGBnt7+1YVTA6Hs3r1ajnvTg4l/xSor683MDAwMVH2shIKgeBVhc/nGxgY0B/dVSN8Pl9fXx/BK59mB9+D/JDps0jUIh2//vqrnP3ySkpKDh06RAjx8/OTNVIP5w8YMIBhAAAAAAAKdPFBaY2gxaP905S+fQrlyq3UuxmPOu0mEokPx18qr6zptKfSID+EHrAz1XO1MZLzH5fN6tZ/8q+mo6X4G5MRERHHjh3z8fF59uxZTExMWFjYgAEDIiIi0tLSWr9ZO7u2p9vb2xNCCgv/uwBrXFxccHCwg4ODn5/fvHnzfv/9dxcXFwMDg7YnduuXW+r61tYquCkCAABqjekTiB999FF8fDyPx5syZcqePXuofJHuzp07CxYsKCkpIYR8+OGHhBCxWHzs2LFNmzYRQmbMmMEwAAAAAAAFOnGngH7o2998oKJ3WuiK6tr6S9fvdrFzQ2PTmcs3586Y2KshdR3yQ+iBea+6yO8wZcuVbl1w+xz/nkfTUxMmTKC2IL906dK1a9fOnTt3/vz5ixcvnj9/PjAwUNatoqKi7bmlpaXkf6W9gwcPLlq0SE9Pb/bs2WPGjPH393d2dmaxWJ6enm3n+HdrmRcrKytCSGVlZQ/eHQAAvMyYFhDfeOONDz74YO/evffv3x85cuTIkSO9vb379evHYrGePn1Kba5H9XzrrbfCwsIIIXPnzv31118JIY6OjvPnz2cYAAAAAICi5PDqHhS1eJQvfLijSiJJvHNfJOrGOlNZuQUlZZXyF0NUGuSH8BJqbGwsLS1ls9mOjo6Ojo5z586dO3cun89fsWLFr7/+umfPHnoBMTOz9Q7RpaWl1KIQ1OKhmzdvJoQcOnQoODhY1kcikTCfIe7s7MzhcIqLi+vr6w0NW9wdWb9+fWVl5ZdffmlmZsZwFAAA0DxMC4iEkJ9++snKyurbb78Vi8U3b968efNm2z4fffTRtm3bqJtjDQ0NhBA3N7czZ86o3UJyAAAAoMGOt3z80NRA+/UhNiqJJPPRk+6e8uBRfh8pIBLkh/Dyqamp8fLy4nA4T58+lS1JaWBgMH78+F9//bXVY4MHDhxYvnw5fT5yTEwMISQwMFBbW1sqlVI7Eck2VqYkJCQw365ES0srKCjoxo0b+/bt++c//ylrz8vL27Jli6Wl5datWxkOAQAAGkkB+7Gy2ewNGzY8ePBg6dKl7u7u9JecnJzmz5+fnJz8448/yrZumDJlyrFjx+7evevq6sp8dAAAAACFEDSK/k7n0VtC/Ry1uCrYvF7Y0FhVW9/ds0pK25kUqSrID+FlY21t7ejoKBaLV6xYUVPz3weZs7Kyvv/+e0JIUFAQvXNFRcW0adNyc3OlUmlNTc3GjRt/+uknQsiaNWsIISwWi/pG2LFjB1VbFwqFBw4ceOedd6jTHz3qfHVUOb788ktCyMaNG2NjY6nrFxQUUJsyz5gxo1sTogEA4OWhgCcQKe7u7lu3bt26dSufz6+oqBCJRObm5u0u6Dtv3jxFDQoAAACgKGfTngkaX2x5zGaxQv1VM3+5ji/swVm1fIHCI2EI+SG8VL7//vvIyMijR48eP37cysqqoaGBWmrQ39+fWutT5v3334+NjfX19TU2NpZtrLx8+fIxY8bIvo6KitqxY8fu3btNTEzKysoIIeHh4Ww2+9ixYxMnTvzkk096vGnyyJEjV61a9e233y5evPif//ynubk5tQG6l5fXunXrevz2AQBAsymsgChjYGDQdnew8vLyqKioQYMGUct5AAAAAPQ1J5NbbBk8cpClvZmeSiLR1upJhqbzv6f5+iDkh8DcmRWvqzqETkyaNOnChQvbtm3LyMjg8XgmJiYBAQGRkZFz5szR02vxj8mMGTMiIiJ27tyZnJysp6fn5+f37rvvTpo0SdZh5syZ+vr627dvf/jwIYfDmTx5cnh4eERERElJSU5OTklJibOzM5NQV61aNWLEiP3796elpVVWVvr6+k6dOnXx4sU6OjpMLgsAABpM8QXEdiUkJJw6dcrCwgIJIgAAAPRBaQVVuaUtdicIC3BSVTBGBvpcDkck7sYmKoQQMxOjXoqnlyA/hD7FwMBA9jBgu0xMTFp1iIyMjIyMpLcEBQUdOXKkK8ONGDGi7QbldCEhISEhIa0a7e3tExMT5QRAWbNmDTUbWk63sWPHjh07tiuhAgAAEIUUEEUi0fbt269cuZKTk9NRh9zcXEKIVh++MQ4AAAAvsxN3CumHtia6o1wtVRUMh8N26Wf/KK+w8640gwaoZsJ1u5AfAgAAAGgSpgVEsVg8derUv/76qyudV61axXA4AAAAAIWrFjQlZJbSW6YNd2KzVbmTgL+XW7cKiIb6eoNdVPbIZCvIDwEAAAA0DNMCYnx8PJUdOjg4jBs3rq6u7uTJk4SQyZMnOzk51dfXX716taioiBBy5MiRWbNmMY8YAAAAQLFOpxQ3iSSyQy6HFeLroMJ4CCHDhgxMTLpfxCvrYv9xo/21tfvKo3zIDwEAAAA0DJvh+fv37yeE9OvXLy0tLTY29sSJE3PnziWEBAQE7N69+9ChQ0+fPl28eDEh5ODBg1KplHnEAAAAAAoklZL4lBbbp7w2xMbSSMU7CbAImR06Tl9Ptyudh7q7jPD16O2Qug75IQAAAICGYVpAfPz4MSFk3rx5FhYWVAu1fdjNmzf/OwCbvX379uHDh589e/bSpUsMhwMAAABQrNu55YUVAnrLdNVtn0JnbmocFTnZxNhQfrdhg11mhryuyunWbSA/BOjIH3/8UVtbO3LkSFUHAgAA0D1MC4jU9BM3NzdZyyuvvEIIefjwoayFxWItWbKEEBIbG8twOAAAAADFarV9Sj9LA99+5qoKphV7G8vFc8MCfYaw2e3kbMaG+tMnvfpW6HgtrgK2xVMg5IcAAAAAGoZpusltk7Da29vr6uoWFBTw+XwDAwOqcdiwYYSQxMREhsMBAAAAKNDz2obrj1qsMxge4MTqS4/zGerrhb8xZvwr/lk5TwpLyp5XVOrqaFtbmA/q7zCwnwOXy1F1gO1AfggAAACgYZg+gejo6EgIycnJeXFFNnvgwIFSqTQ1NVXWaGZmRgjh8XgMhwMAAABQoLjkIrHkxRp8OlqcSd72KoynI8aG+kG+HjMmvxbymn9Y8KiQcSPdBzr3zeohQX4IAAAAoHGYFhA9PDwIIfv27auoqJA1uru7E0LOnTsna8nNzSWEWFlZMRwOAAAAQFHEEunpe8X0lglDbY31+spexuoL+SEAAACAhmFaQKQWrykqKvLz8/viiy+oxnHjxhFCdu3a9eDBA0KIQCDYuHEjIWTQoEEMhwMAAABQlKvZz8tqG+gt4QHOqgpGkyA/BAAAANAwTAuIo0ePnj59OiGkoKDgq6++ohojIyNNTEyqqqq8vb09PDzs7Oyo/fUWLFjAcDgAAAAARTnZcvsUN1ujIfbGqgpGkyA/BAAAANAwTAuILBbr8OHDmzdvHjVqlI6ODtVoYWGxd+9eDocjFouzsrJqa2sJITNnzoyIiGAaLwAAAAADgkZR6tOqxMfVZ9KfJ+dX0F+aEYTHDxUD+SEAAACAhmG6CzMhRFtbe8WKFStWrGhqapI1zpw5083Nbc+ePbm5uc7OzuPGjZs1axarT21qCAAAAC+TzOKaf13NvZ1b0SyStH3VUJcb7GWn/Kg0FfJDAAAAAE2igAKijLa2Nv3Qx8fnp59+UuD1AQAAAHpAIpHu/Pvh0VtPpdIO+3g4mOpp99FNjdUa8kMAAAAADcB0CvOWLVu2bNkikbRzJ5+uoKBgy5YtsbGxDIcDAAAA6BaplKw5fv/ITXnVQ0JIUm75kRtPlBSTpkN+CAAAAKBhmBYQo6Ojo6OjO00Qy8vLo6OjV69ezXA4AAAAgG7597XcSxm8rvTceeHRnbyKzvtBZ5AfAgAAAGgYpgXErhCLxVevXiWElJWVKWE4AAAAAEppTcO/E/O72FkikcaczZZI5D6pCAqC/BAAAABAjfRkDURLS8tWLTY2NnIWwBYKhQKBgBDi4ODQg+EAAAAAeub4nYLGZnHX++eX1d/IKR/tbtV7IWkq5IcAAAAAGqwnBcSKitazeyorK7ty4scff9yD4QAAAAB6JjG720+3XXv4HAXEHkB+CAAAAKDBelJA/PLLL2Vfr1u3jhCyZs0aNlvebGgtLa3AwMDg4OAeDAcAAADQAxKJ9GkFv7tn5ZXW90YwGg/5IQAAAIAG60kBce3atbKvqQTxiy++4HJ7cikAAACAXlItaO7BgoZV/MbeCEbjIT8EAAAA0GBMs7oFCxYQQuQscAMAAACgZJnFNRczeJcedGnz5VYMdbUUHs/LBvkhAAAAgIZhWkDcvXu3QuIAAAAAYCiHV3cxg3fxAa+4UtDjizia6yswpJcT8kMAAAAADaOYeSVSqTQ/Pz8nJ0cq7WSi0MSJExUyIgAAAAAlv6z+UgbvQgbvaXm3Vzxsa9Sg1rsJQ88gPwQAAADQGAooIN64ceMf//hHfn5+Vzp3mkECAAAAdAWvWng1+/mlB7z7BdWKuqapvvbrHjaKutrLDPkhKIGkpqZ8eoT+jOmGHy5QdSwAAAAajmkBMS8vb+zYsU1NTQqJBgAAAEC+0pqGK1mllx7w0guru1J30tPmCJvEXbz4+2MHGuhg3w+mkB+Cckh4pZKKClFWtqoDAQAA0HxMU+R169ZR2eGbb765YMGC/v37s9lsRQQGAAAA8EJZbcPlzG7UDW1NdF8dYjPO02aQjdH7+2/nPq/v9JQJQ+3CA5wVEOtLD/khAAAAgIZhWkC8ffs2IWTKlClxcXHYaw8AAAAUq1bY/J+HZZcf8G4+LhdLOi8cWhvrvu5hM87TZpiTmSwx+eHdgE+PpqYVVMk5MdTfccWUIchlFAL5Iai79PT0X3/9NSEhobi4uKmpycbGJiAgIDw8fMqUKV35SP/222/vv//+/Pnzt23bpoRoAQAAlIBpAfHJkyfk/7N35wFRlXsfwJ8zwywMMAwwbIKCCLgAKuYupuaeds1yK5eyMMVu1rVM225lmWY39fbmWmYugeZ608qrmbggBijgvrDJIjvMALMv5/3jFHcccJhhZhgGv5+/4DnnOc/vDDA88zvPQsiCBQvQOwQAAABbqVdozt2u/P162cXcKq2u5byhSMAd2ctvYp9OhnnDRl5u3I0vDjiYXrjjbJ5EZjyvtquv++KxEcO7+9kqeED/EJyXWq1esWLF9u3bDZfmvHfv3r179w4cODBgwIDvvvsuJCTEsMqO1GRvAAAgAElEQVTq1at5PN7SpUvbPFgAAIC2Y20C0cvLq6yszMfHxybRAAAAwKOsQak9e6vi9+tlf+RWa3T6Fs8XunKGRfqOjgoYEiFms0zlqlzY1MzBIdMGdrlcUHO9WFpYXuPCZocGePXv6h0ZKLTdHQAh6B+C09JoNNOnTz99+jRFUfPmzXvhhReio6NdXFzu3bv3008/rV+/Pj09fcyYMb/++mt4eHhjrdWrVwuFQiQQAQCgY7M2gTho0KD//Oc/ly9fjouLs0lAAAAA8KhRaXTpeTWnrpedvlGu1LS84YkH3yWuu9/oqIDB4WIXtgVj3NgsakCYz4Awn5ISHo/HE4vFVkQND4X+ITipVatWnT592tXVNTExcfTo0Y3l4eHhS5cufe6556ZMmXLr1q358+efOnWKy+U6MFQAAIA2Zu2C1kuXLqUo6quvvqqtNbWuEAAAAIARtVZ//nblx4euTlh7+q3Ey79m3zedPeRz2KOjAv71fL9f3h714TMxcd19LcoeQptB/xCcUXFx8VdffUUIee+99wyzh40CAwN37drFYrGys7MPHDjQ5gECAAA4krUJxMcff3z9+vW5ubmTJk3KysqySUwAAADQzulpuqhafr1Udv1+Q0mN3JxtkRtp/sobTvwrb6hQm8ob8jjsuEjff06N+fXtUatm9Inr7sthY0vfdg39Q3BGO3bs0Gq1wcHBr7zyysPO6dGjx+zZswkhW7ZsIYTMnTtXKBQSQurq6oRCoa+vr9H59fX1y5cv79mzp1gsjo2NfffddyUSidE5FRUVy5YtGzVqlL+/f1RUVHx8/JUrVwxP2Ldvn1Ao3LZtm0wmW7BgQWBg4IcffmiTWwYAADCftVOYjxw5EhISsnDhwq1bt8bGxg4bNqx79+6dO3d2cWn+yu+//76VLQIAAIADVdYpd53P//16eXWD6q+yu/6e/NFRAfPiuorcHjqnT6+nrxZLTl0vP3GlVCI33smkKa4La2CYzxNRAaN6+bty2TYKH9oC+ofQCtriYrq+3qIquoJ7tFarl0o1N29a2pxL164Un29YcurUKULIiy++yH+w3MiiRYt2796dnZ1dW1s7fPhwNze3xMREDoczffp0DodjeGZdXd24ceOuX78eGhoaGBiYm5v79ddfZ2ZmHjt2jM3+8z3twoULL7zwQnl5OSHE19e3qKioqKjo0KFDW7ZsmTFjhuHVaJpOSEg4cuSIpXcKAABgE9YmEKdOnWr4bUpKSkpKionz0UEEAABwXoczijYcv61qMtG4XKpMvFBw5FLxiqd6jYsJNDykp+mrRZJT18tPXi2tbbIDclMsFvVYqPfEPp1G9PRz41nbUQGHQP8QWqHh318pfztlWR2tlpZIlOfOqbOzLW1OvDfJpXtk47c0TTPj/nr06GG6YkREBEVRNE1nZWUxYxUTExNdXV2ZMYmGDhw4EBoampqaGhUVRQj5+eefZ8+enZKSkpGRMWjQIEKIQqGYP39+eXn54sWLV6xYIRKJJBLJ2rVrv/7664SEhNjY2IiIiMarbdu2raCg4M033xw+fHh0dLSl9wsAAGAla/vlbm5uNokDAAAA2rlvTudsT841cYJcpf3w4JXqBvVzQ0Ia84a/Xy+rqleZqMVgsajoYM/RUQHjYwJNDGMEp4D+IdiGTkcrFCaO03o9oWmi0dINDaauw2JTAlfTTdXV1Wm1WkKI4fbKzeLz+cHBwUVFRdXV1abPJIRs376dyR4SQiZNmjRu3Ljjx4/fvn2bSSBu3LixtLR05syZa9asYc4RiUSfffaZRCLZs2fPhg0bNm7c2Hipu3fv7tu3b+LEiS02CgAAYA/WJhAbTP+3BgAAgA7h5NVS09lDBk2Tr/57+/Z96dUiSUmtqU/+DBZFRXf2HB0VMDY60NsdecMOAv1DsAlapaKVSpNnEJqmKZ2uhdMIoVz5hDK155JGo2G+MD1/mcGse6gwmdwkhMTExAwYMMCwpFu3boZtnThxghDSdMnF+Pj4PXv2GI3bjYyMnDBhQouxAQAA2AlmBgEAAEALFGrdhuO3zTyZpunjV0pbPK1HJ+HEPp1GRwWIPXjWRQePuvv37y9ZsqRnz56ffPKJ0SGpVJqUlJSWliaRSHx8fIYMGTJr1iyBQGDOZa2p20ipVMbHxw8bNiwhIcGiikAIoVxdyYOrChrT6Uh9PeFwKJODXikWy3T2kBAiEomYick5OTlhYWEm29Tl5uYSQnx8fExfs+lgRhbrgQ2gmOusXLnSaHlQJsN4//59w8LIyEiqpbsAAACwH7skELVaLYvFMvoHaRPoIAIAALS9X7JKDLZMsUpXX/fRUf4T+wYFebUwoxA6GDv1D2ma/r//+z+1upnlNaurq5ctW1ZVVUUI4fP55eXlR44cycjIWLt2rbu7u+nLmlk3Ozt77969eXl5YrE4Li5u2rRpRttoHD16VKFQGO2GAc1iefu4BAdbVIVWKLU5dyl3D5fQEIvb4z7wk3JxcenWrVtOTs7du3fHjRtnol5RUZFSqSSEREZGmjiNECISiUwcVSqVlZWVhJAzZ8487AS1Ws3l/jk0u8V8JQAAgF3ZLIF44sSJH3744ezZsxUVFXK5vKioKDg4+MyZMzdv3pw+fbpN/uGhgwgAAOAQZ25VWHmFyEDh2OiA0VEBnZA3fJS0Qf/w+PHj169fb/bQtm3bqqqqwsLC3nzzzeDg4Ly8vNWrVxcXF+/atWvx4sWmL2tO3eTk5HXr1vH5/IiIiPLy8qSkpJs3b3788ceNw8RkMtmhQ4eefPJJpH7MIXxnuaVVtLfvVM16jv/446J/r7c+gJEjR+bk5OzduzchIcFEpnvv3r2EkODgYGY+sgmmBwzyeDxPT0+pVFpYWGg61cho3LgZAADAIWzwEFilUs2ePXv8+PG7du0qKCiQy+WNh/Lz8xMSEjp16rR9+3brGzKng7hx48Z9+/atX7/ez8+P6eS1eFlz6iYnJ3/wwQe5ubnh4eEqlSopKemTTz6habrxBHQQAQCgA8spr29dxa6+7vEju+17LW7XoiFz47oie/joaJv+YVVV1ffff9/sIalUmpaWxuVy33nnnc6dO1MU1a1bt3fffZcQcvbs2WYfSFtUV61Wb926NSAgYMuWLatWrdq6devEiROzsrIuXLjQeJ0jR45otdpp06ZZeZvQNubPn08Iyc7OTkxMfNg5xcXF69evJ4TEx8dbOaGY+b0ihNy6dcvokEKhyMvLKykpseb6AAAAtmVtApGm6YSEBOa/rLe399SpUw2Penl5URSlVqvj4+M//fRTaxpCBxEAAMAh9HpaItNYWovrwvrh1WFJfx8WPyo8RIw9eR8tbdM/pGl606ZNCoVi9OjRTY+eO3dOp9P17t3b39+/sTAsLCwsLEwul2dkZJi4sjl1CwoKZDLZ1KlTvb29CSFsNvuFF14ghFy7do05v66u7j//+c9TTz3l6enZ6nuEthQTEzNz5kxCyLvvvtvsb0htbe2LL76oUCi6dOmyYMEC61uMi4sjhGzevNmofN26dX379m1aDgAA4EDWJhAvXLiwY8cOQsiECRPu3r176NAhw6NTpky5devWkCFDCCErV67My8trXSvoIAIAADgKi0XxOBZ3GEQCbje/FlYRgY6qbfqHZ86cycjIePzxx/v379/06NWrVwkhsbGxRuX9+vUjhGRnZ5u4cqvrUhSl0+mYrw8dOsRisYySp9DObdiwoU+fPhKJZNy4cW+99VZGRoZMJqurqzt//vy6dev69++flpYmFAp3797t4eFhWLGhoaG+3uKR2kuXLhUKhYcPH16xYoVUKiWEaDSaHTt2fPnll1wud968eTa7MQAAAKtZm0DcsmULIcTPz2/v3r1Mfs1IZGTkyZMnu3TpotFo1q5d27pW0EEEAABwoE4ii6ceB1peBTqMNugfSqXSb775xsPD42EDwWprawkhnTp1MioPDAwkhEgkEhMXN6duaGioQCA4fPhwdXU1IUSn0+3cuZOm6ZiYGOYKx44dmzJlilGaCdo5Nze3X3/9derUqVqtdtu2bU888URgYGBwcPCTTz750UcfVVZWRkVFnTx50uizg7+/v16v79ev38iRIy1qztvbe8uWLR4eHps2bercuXNkZGRQUNDrr7+u0+k2b97c4iYtAAAAbcnaBGJ6ejohZP78+SYG37m5uS1cuJAQkpmZ2Yom0EEEAABwrP5hzeSATBsSIbZHJOAU2qB/uG3btvr6+gULFjysCWY8l5ub8fR5Zos85ujDmFOXy+UuWrSorKwsISHhvffeW7hw4a+//tqvXz9mUur+/fu5XO6UKVNacWvgWO7u7jt37jx16lR8fHxkZKS7uzuPxwsODp4yZcp33313/vz5nj17GlXZsGFDSEhIbW2t6c8dzZo8efKFCxfmzJkTExMjlUo7deo0bdq0ixcvTp8+3UY3BAAAYBvW7sJ87949Qkh0dLTp05gHaE1XCDYH00FcunSpYzuI69atS0hIYHbZq6ioQAcRAAAeERV1yoy8GouqcFxY43sH2ikeaP/s3T9MS0s7d+5cv379RowY8bBzmF4c06MzZH7/sMW6I0eOFIlEe/fuzcnJ8fHxee6556ZPn05RVGVl5fHjx59//nmBQGDmHdXV1aWmpjYtp2laKpUyr6cJNTU1zEPx9qDZMadOZ8CAAQMGDDDz5EmTJk2aNMmwZObMmcxyikZWrVq1atUqo8KQkJBNmzaZuP7DrtZ+tPgrals0TbcuXdseIHhHoWlaIpGYfvNvt/R6PYJ3iI4dvOG2wGayNoHI4/GUSqVMJjN9Wmlpaeuu3/E6iDKZjJk3bUSn08lksrKyMjOv06iurq7F1799kkgkdXV1jo7CMnq9nhBSU1PDYtlgB/O2xMy4r6qqsnLHwLbHRF5RUeF0kWu1WkJIK/6oHU6j0ej1eieNXKfTOWPkarWapun2Gfn1UtkXJwokcq1FtZ6M8qGU0rKy9tvf0mq1TvrbotfrZTKZSqUyv0orOohWsmv/UCaTbdq0ic/nL1682MS/BtN3rdGY2hfI/Lp9+/bt27ev0Qk//vijm5vb5MmTTVzECI/HCw8Pb1qem5vL5/NFIpGJujU1Na6urnw+3/zmAGzL9K+ozdXW1jrv77xT/8E6dfC1tbV8Pt/V1SnXV0HwjtKxg2eSGxaxNoEYHh5+6dKljIwMZhLKw5w5c4YQ0rVrV4su3iE7iBRFcTichx1qRVqqdbUci3nZnTFyhjNG7ryvORM5i8VyugQiE7DTveAEkTtI+/zz/O/16q3nirQ6y9JPPQLcXhgSxGK197/Z9vmam8miyNs+gWjX/uGOHTtqamoWLFjg5+dn4jSRSKRQKJomMRsaGgghXl5edqpbVlZ28uTJl156ifmMLZfLd+/enZGRIZfLIyMj582b1+z98ni8bt26NS3Pz8/n8Ximt+ljPtILhUIT57SlVuwl0nosFiGEbu9vNh1fG+8kyXwqdtLtKxG8o0gkEgTvEAjeUVoM3gEJxClTply6dGnnzp1vv/12REREs+ccPHjw4MGDhJCJEydadPEO2UEUCASPPfZY0/LffvtNIBCYvlMjNE3LZDIPDw+nW35Rp9MVFhZ6enqaP3KznVCr1SUlJV5eXk735E2hUJSVlfn4+Dwsf91uyWQypVIpFovZbLajY7FMXV1ddXW1RX/U7URtbW19fb0zRl5dXa1QKJwx8oqKCp1O164iV2l0nx+78UvWfeMDFCEmk1H9u3p/NrOv0LW9v9WUlJTweDyx2PkWaiwsLHRzczPdRTHSig6ilezXP7xz586JEyciIyONZow2JRKJSktLm/bxmJIW+4etrpuUlCQSiSZMmEAIUalUy5YtKyoq6ty5s7e3d1ZWVlZW1urVq3v06GE6eDATOzREMGsm/+ETlQAAAMBWrH3w/tprr4nFYo1GM3LkyFOnThn1UIuLi1999dVZs2YRQgQCwZIlS8y/skUdRPJXl86QmR3EVtdt2kH8+eefuVxucHBwVlbW0qVLW7fmIwAAgGMV18hf+uaPptlDVy575bN9Fo2OcOM18wDSU8B5fUKPf8/r3/6zh2Bv9usflpeXE0Lu3Lnz9NNP/+0vn3/+OSEkOzub+TYnJ4f81cdrOkuauYLpGZetrltUVJScnDxz5kwul0sIOXz4cFFRUXx8/Ndff/3555+vX7+ey+UyW1SDTVAcjnD529zBgxwdCAAAQMdn7QhEkUh06NCh8ePH379/f8yYMY1P8idPnlxYWNi4nDNFUTt37mS2NjaTYQfR6BDTQSSErFu3Ljw83IEdxEWLFhl1EJ966imKogoKCpYvX75ly5YNGzaYdbcAAADtQ+rdqn8evFKvMF4ApLOPYM2s2G5+7oSQmYNDLuZUZRfWFlVIKYp09hP1C/UaGObD4zjZSGGwE/v1DzkcTtPumVqtlsvlbDabmZPBzO+OiYlJTU3Nzs42ehSdlZVFWtrgpdV1ExMTfX19x44dy3x79epVsVjMdA4JIaGhoWPGjDl69KhcLne6SRgAAADwiLM2gUgIGT58eFpa2ssvv5yWllZVVcUUZmdnN54QFhb27bffjho1yqLLooMIAADQlmia7EnJ3/zbXX2TJfOGRfp+9GxvD/6f3QZXLntUL/9RvfzLyspYLFa7mnwN7YSd+oeDBw8ePHiwUWFKSsrnn38eHR39ySefGAawffv27OxsqVTauABQWVnZnTt3BAJB04sYBd+Kunl5eSkpKUuWLHFx+V8Hu9n5420/qRwAAADASjZIIBJCoqOjL168mJqa+ssvv1y+fLmqqkqr1Xp7e0dFRY0dO3bixImtWLwMHUQAAIA2I1dpPzly7fSNcqNyiiJzhnVNGBPBcraNjMDh7NE/NJ+np+fAgQNTU1P/9a9/vfPOOwKBQCqVrlmzhqbpUaNGMTNIGPv379dqtb17946KirK0rqHExMROnToZpkRjYmKuXr166NChZ555hqKovLy83377LSwszN3d3X43DgAAAGAPtkkgEkIoiho6dOjQoUNtdUHzoYMIAABgjcJq2fKkrPzKBqNyN57LP5+JGdEDAwyhlRzYPySEvPLKK3fu3MnOzp49e3ZQUFBRURFN0126dJk7d67hafv371cqlSwWq7F/aH7dRnfu3ElLS3vrrbcMs6JTp049d+7czp07//vf/3p4eOTm5lIUtXjxYjvdLwAAAID92CyB6FjoIAIAALTO+TuVHx280qDUGpWHiN0+nxUb6uvmkKgArOfj47Nhw4bExMS0tLSSkhJfX9+4uLiZM2e6urravO6ePXu6dOkSFxdnWMjj8b744ovdu3dnZGSUlpbGxsa++OKLISEhtrk9AAAAgDZkmwSiVqs9duzY2bNnIyIiEhISGssXLVp0//79sWPHLly48GGj+WwCHUQAAABLmVj0cHh3v4+ejWl2t2UAM7VZ/3DYsGE//fRTs4c8PT0TEhIMW2/qxx9/bHXdRitXrmy2XCAQLFy4cOHCheZcBAAAAKDdssEHg8LCwtmzZ58/f54Q8uabbxoekkgkR48ePXr06M6dO5OSkiIiIqxsCx1EAAAAm5CptB8funr2VoVROYtFJYyOmDOsK9Y8BGu0Zf8QAAAAAOyNZWV9tVo9evRopncoFArDw8MNj44fP75Xr16EkEuXLj3zzDNarfH0KAAAAGh7OeX187akNs0eego46+c8NjcO2UOwCvqHAAAAAB2MtQnEzZs35+TkEEKWLl1aWVm5aNEiw6Pz58+/du3apk2bCCHXrl3bunWrlc0BAACAlU5eLV3w7R8lNXKj8sgAjx2vDBnUzcchUUFHgv4hAAAAQAdjbQLx8OHDhJAnnnjiX//6V7Or2FAUlZCQMHPmTELIsWPHrGwOAAAAWk2vpzeevPPPg1cUap3RofG9A7fFD+rk1fLawQAtQv8QAAAAoIOxNoF49+5dQsizzz5LmZzsNG7cOELIzZs3rWwOAAAAWkcq17y++9Lu8/lGO6awWdSrYyM/frY3n8N2UGjQ0aB/CAAAANDBWLuJSlVVFSFEKBSaPo3Z0bisrMzK5gAAAKAV7pTWLd+bVSpRGJWLBNxPZ/Tp39XbIVFBR4X+IQAAAEAHY+0IxMDAQELI5cuXTZ926dIlQoi/v7+VzQEAAICl/nul9JXtaU2zh90Dhd8vHIzsIdgc+ocAAAAAHYy1CcQnnniCELJnz578/PyHnVNUVLRr1y5CyPDhw61sDgAAAMyn09MbT9758OAVpcZ40cOJfTpte3lggAiLHoLtoX8IAAAA0MFYm0B87bXXKIqqrKycOHHiyZMnaaN1lQhJSUmZPHlyZWUlIWThwoVWNgcAAABmksjUS3Zl7D5vnMHhuLD+MbHHh8/E8LDoIdgH+ocAAAAAHYy1ayDGxsZ+9NFHH3744e3bt8eNG9erV6++fft26dKFx+MVFhZeu3YtPT2dOXPx4sV4wgwAANA2bt2vW7E3s0yqNCoXe/BWz+wb01nkkKjgEYH+IQAAAEAHY20CkRDywQcfCIXCFStWqFSqGzdu3Lhxw+gEiqLeeuut1atXW98WAAAAtOhIRvGXv9zU6PRG5b27iD6b0VfswXNIVPBIQf8QAAAAoCOxdgozIYSiqDfeeCM3N/fDDz8cNGgQl8ttLO/Ro8ff//73q1evrl27ls3GPCkAAAD70mj1n/10fc3R602zh0/3D9744gBkD6FtoH8IAAAA0JHYYAQiIygo6KOPPvroo49omq6vr9dqtZ6enugUAgAAtJnKOuWKfVnXi6VG5VwX1rJJvZ7qF+SQqOBRhv4hAAAAQMdgbQLx66+/JoR079597NixTAlFUUKh0Nq4AAAAwBJZ92rf+zG7ukFlVO4n5K+Z1bdXkKdDooJHE/qHAAAAAB2MtQnEN954Q6fTjR49urGDCAAAAG3sSEbxv365odUZ73UbG+q9anofb3euQ6KCRxb6hwAAAAAdjLVrIPbs2ZMQkpOTY4tgAAAAwDJqrf6TI9fWHL3eNHv4dP/g/5vXH9lDaHvoHwIAAAB0MNYmED/44ANCyL17937++WdbxAMAAADmKpcqF27/4+fMEqNyVy571fQ+K56KcmFTDgkMHnHoHwIAAAB0MNYmEGfMmLFhwwYWi/XKK6+cOXPGJjEBAABAiy4X1Ly4NfXm/Tqj8mBvwbfxg0ZHBzgkKgCC/iHYn16vzy8qvZh548S5jLNp2Zk3cqT1MmsuuHfvXqFQKBQKa2trjQ7dunWLObRhw4amFZ988kmhUDhnzhxCyL59+4RC4RtvvGFNJAAAAO2TtWsgHj9+vHv37u+9995nn302cuTI/v37R0VFBQYGurm5NXv++++/b2WLAAAAjziaJntS8jefuqvXG09bHhIhXvlsbw9XjkMCA2Cgfwj2o9Fq07JuXcy6oVSpG+QKptBd4EoICQnyHzUkNshf3IrLDhs2jPni0qVLY8aMMTx09uxZ5ovTp08bJQd1Ot3ly5cNq5vjjTfe+O677yIiIi5dusSUrF69msfjLV26tBWRAwAAtA1rE4gTJ040/DYjIyMjI8PE+eggAgAAWEOh1n165Nqp62VG5RRF5gzrmjAmgkVh2jI4GPqHYCeSuob9vyRXVEtkcgWLxeZxORShaEKrNVqtVnv9bkHh/YqRg/sO7Rdl6ZU7d+4cHBxcXFyclpZmlEBMTk5mvkhNTVUqlXw+v/HQzZs35XI5IWTo0KHW3Nfq1auFQiESiAAA0J5Zm0B82JNkAAAAsLniGvnypMzcigajcgHP5YOno0f18ndIVABG0D8Ee2iQK3Yd+m9pZQ2bxXbl81msBx6WcFxctDptvUx+OjVTq9U9PrC3pdePi4vbu3dvWlqaYaFOpzt37hyLxQoLC8vJyfnjjz9GjBjReJTJjHt4eERHR5vf0NixY318fHx8fCyNEAAAwIGsTSA2NBh/hgEAAAB7uHC38sMDV+qVWqPyLj5unz/Xt6uvu0OiAmgK/UOwOZomh46fK62scWG78LicpiOtKYpwXFzYLJZMrjifcSU40Desc6BFTQwbNmzv3r0ZGRl6vZ7F+nOl+CtXrkil0n79+o0YMWL9+vXJyclNE4iDBw9ms9nmNzRp0qRJkyZZFBsAAIDDWbuJCgAAANgbTZPd5/Pf+iGzafYwLtL3u1cGI3sIAB3b7bzCotIKFkXxec1kDxuxWCwel1svU5xKuUzTxqvEmhYXF0cIqauru3PnTmMhswvQyJEjR44cSQg5ffq0YRUmgdh0AcT6+vrly5f37NlTLBbHxsa+++67Eomk8ajhXitz584VCoVMu0Kh0NfX1/A6FRUVy5YtGzVqlL+/f1RUVHx8/JUrVyy6KQAAAFuxdgRis7RaLYvFanxwBwAAAK0mV2lXHr6WfLPcqByLHoJzQf8QTJPJFWqN8TOSRhczb9TL5HwuV6/Xm74Oi0WxKFZZZfXNnHuBfg+dJuzp4Wb02xgWFubv719eXp6ent6jRw+mkFkAccSIEYMHD+bxeJmZmRKJRCQSEUIaGhpu3rxJmiQQ6+rqxo0bd/369dDQ0MDAwNzc3K+//jozM/PYsWNNByoOHz7czc0tMTGRw+FMnz6dw/nfFlgXLlx44YUXysvLCSG+vr5FRUVFRUWHDh3asmXLjBkzTL8IAAAANmezBOKJEyd++OGHs2fPVlRUyOXyoqKi4ODgM2fO3Lx5c/r06VjjAwAAoBXuVcmW780sqJQZlbvxXD58JubxHn4OiQrATOgfgvmOn02/lVv4sKPMhssqtYaY8cSEpolModx37DSX+9At6eNnTvIXexmWUBQVFxd38ODB9PT0uXPnEkLUanVqaiqHwxk8eLCrq+uQIUOSk5PPnj37t7/9jRBy+fJlmqb5fH5sbKzhdQ4cOBAaGpqamhoVFUUI+fnnn2fPnp2SkpKRkTFo0CCjMDQ0WmsAACAASURBVF555RVCSGJioqur65YtWxrLFQrF/Pnzy8vLFy9evGLFCpFIJJFI1q5d+/XXXyckJMTGxkZERLT8QgAAANiODR4Cq1Sq2bNnjx8/fteuXQUFBcxOZIz8/PyEhIROnTpt377d+oYAAAA6Hj1N18o1tXKtTm882+7c7YqXv7nYNHsY7u+xa9EQZA+hPUP/EGxI3/j2aN54a+YsvYVTmMlfmyk37qOSnp6uUCgGDhzo6upKCGFmMTduyszMXx4wYACXyzW6zvbt25nsISFk0qRJ48aNI4Tcvn3b/Eg2btxYWlo6c+bMNWvWMAMeRSLRZ599NmfOHI1Gs2HDBktvDQAAwErWJhBpmk5ISEhMTCSEeHt7T5061fCol5cXRVFqtTo+Pv7TTz+1si0AAIAOQ6ujj2QU//379OErT76w49pLu24MX3nyle1/7E29p9To9Hr629M5bydlNjRZ9HBsTMA38YOCvAUOCRvAHOgfgm3RxOJUICHE0jUQyV/LIN68ebO+vp4QcvbsWULI448/zhwdNWoUIeT3339nvn3YAogxMTEDBgwwLOnWrRshRKPRmB/JiRMnyF/jEw3Fx8cTQlJSUsy/FAAAgE1YO4X5woULO3bsIIRMmDDhhx9+8Pb2pgxWYpoyZcqtW7defPHF1NTUlStXPv/882FhYVa2CAAA4OyuFkk+OnS1pEZuWKin6SuFkiuFkj0p+WJ33q3SOqNaLBaVMDpizrCuWPMQ2jn0D8G2qFa967WiVvfu3b29vWtqai5dujRy5EhmsCEz8JAQ0rt3by8vr7y8PGYyPpNAZAYtGgoPDzcqacXSn7m5uYSQlStXurg88HmNyULev3/f0gsCAABYydoRiMxSHX5+fnv37vX29m56QmRk5MmTJ7t06aLRaNauXWtlcwAAAM4u+Wb54u/TjbKHhqrqVU2zh54Czr/nPjY3DtlDcALoH4JtsRpzgeaNKWTOakXajsViNc5ilsvl6enprq6ujz32GHOUzWaPGDGCEHL69OmSkpKysjIXF5eBAwcaXYSZcWwNpVJZWVlJCDlz5sypBzGDIpVKpVqttrIVAAAAi1g7AjE9PZ0QMn/+fE9Pz4ed4+bmtnDhwvfeey8zM9PK5gAAAJza7dK6jw5e1Whb2EXUSGSg8PNZfQNFrnaKCsC20D+EVnh2wuMmjh46fi796i0el8NxaeHzC00TuVLp5uq6YNYkPx8v0yc3FRcXd+zYsfT09NTUVK1WO2LECMMlDkeOHHnkyJHk5GShUEgIiY2NFQiMF5Ro3XhJQzwez9PTUyqVFhYWWp+OBAAAsAlrRyDeu3ePEBIdHW36tMjISELIrVu3rGwOAADAedE0+fzoDaVGZ1Gt8b0Dt708ENlDcCLoH4LNDezbw13gqlZrW1wPUavV0jTdtXNgK7KH5K81DdPT00+fPk0IYYYcNmKWQUxOTmay5E3nL9sERVHMsolN/zoUCkVeXl5JSYk92gUAADDB2gQij8cjhMhkxhtEGiktLbWyIQAAAGf3R27VjRKpRVVGRwV8/GxvPodtp5AA7AH9Q7C54ADf6O5d3QR8hVJtYncUnU6n0mhEQvcxw/q1rqHo6GgPD4+ampq9e/cSgwUQGaGhoZ07d66qqtq3bx9pbgcVW2G2c9m8ebNR+bp16/r27du0HAAAwN6sTSAyiwQzSwibcObMGUJI165drWwOAADAef1+o9zSKioLhysCtAfoH4I9PDlyUCc/HwGfp1CqtDrjhSBomlarNQqV2sNN8NQTQ1s3/JAQwmazhwwZQgipqKjw9PSMiYkxPEpR1BNPPMEcpShq8ODBrWulqYaGBmbrZ8bSpUuFQuHhw4dXrFghlUoJIRqNZseOHV9++SWXy503b56t2gUAADCTtQnEKVOmEEJ27tx59+7dh51z8ODBgwcPEkImTpxoZXMAAADO60axZcMPCSHXLRyxCNAeoH8I9sBxcZn99JgeYV0ErnylSiVTKJVqtVqjUas1SqVKJleqtVofkXD6kyN7RYRY0xAz+o8QMnz4cDbbeAB445jE6OhoWy1Q6O/vr9fr+/Xr13hxb2/vLVu2eHh4bNq0qXPnzpGRkUFBQa+//rpOp9u8eTMz/R8AAKAtWZtAfO2118RisUajGTly5KlTp/T6Bx4GFhcXv/rqq7NmzSKECASCJUuWWNkcAACA86pqUFlaRSJXa3XmbTsK0G6gfwh2wuVwnp34+IxJo3qEdXEXuPK5XC6Hw+Vy+Hyer49o1ODYxXOmRIQGWdlK48RkowUQjQqZgYo2sWHDhpCQkNraWolE0lg4efLkCxcuzJkzJyYmRiqVdurUadq0aRcvXpw+fbqt2gUAADCftbswi0SiQ4cOjR8//v79+2PGjBGLxUz55MmTCwsLa2trmW8pitq5c2dgYKCVzQEAADgvLtvi53ZsFsWy9mEfQFtD/xDsKiI0KCI0qEGuKKuskSuULmwXT6FboK8Pi2Xt9seMAQMG1NXVPeyoWCx+2NGZM2fOnDmzafmqVatWrVpl4rRJkyZNmjSpacWQkJBNmzaZGzcAAIA92eBDyfDhw9PS0gYOHEgIqaqqYgqzs7Mbe4dhYWGnTp2aNm2a9W0BAAA4L39PvqVV/IR8FmWbj8QAbQn9Q7A3d4FreEhQ7x7dekWEBPmLbZU9BAAAgGZZOwKRER0dffHixdTU1F9++eXy5ctVVVVardbb2zsqKmrs2LETJ05sunoIAADAo6Z/mM/VIknL5z1YxU7BANgb+ocAAAAAHYZtEoiEEIqihg4dOnToUFtdEAAAoIMZE+X//Zlci1Y0nNgbszvBiaF/CAAAANAxYF0lAACAtqDU6DaevGtR9nBwuDg21NteAQEAAAAAAJjH2gRiXV0dTWN3SAAAAFPqldrXd126cLfS/CpiD977T0fbLyQA+0H/EAAAAKCDaU0Csaqq6p///Ofw4cN9fX09PT1FItGwYcPWrl2rVqttHh8AAICzq2lQv7ojLbuw1vwqASLXf899TOzBs19UALaF/iEAAABAB2bxGoiXL1+ePHlyaWlpY0ldXd2FCxcuXLjw/fff79+/PyoqyqYRAgAAOLFSiWLJroyiarlReXiAR0Flg1ZnPEqLRVET+wT+fVx3LzduW8UIYC30DwEAAAA6NssSiFKpdMqUKY29Q7FYHBoamp+fX11dTQi5efPmrFmz0tPT+Xy+7SMFAABwNvmVDUt2XaqsUxoWUhR5eUS3+FHhZVJl8o3yywU192sadDp9kI9H7y5eo3r5d/YROCpggFZA/xAAAACgw7Msgbht27bi4mJCSGRk5Pbt2+Pi4gghNE2npKS8/PLLd+7cuXbt2pdffvnee+/ZJVgAAADncaNE+o89l6RyjWEhi0W9PanX0/2DCSEBnvxZQ0JmDQmpqKjQ6XSBgdhwGZwS+od2RdO0Xq+3/hwA+2n7Xz+n/p1H8I6C4B0FwTuK6eBbcV+WJRDPnz9PCOFyuceOHYuIiGAKKYqKi4v76aefevfurVark5OT0UEEAIBHXHpe9fK9WXKV1rCQw2Z99EzM6OgAR0UFYA/oH9qPXq9XKpU1NTWmT5PJZBqNxvQ5bYbHw+Ktj5wWf0Vti6ZpuVyu1WpbPrX9QfCOwgSv0+kcHUhr0DStUCgQfNvr2MG3Yr87yxKIt27dIoRMmTKlsXfYqHv37k899dTBgwczMzMtDQIAAKAjSb5Z/sGBKxrtA4/1XLns1TP7Dg4XOyoqADtB/9B+WCyWq6urWGzqfaO+vt7d3V0oFLZZVKbV19c7OgRoa6Z/RW2uoaHBzc3N09OzLRu1FQTvKDKZzM3NTSQSOTqQ1pDJZAKBAMG3vY4dvN1HIObm5hJCevbs2ezRXr16HTx4kFnvBiyl1+sVCkVVVZWlFRsaGlQqlT1Csh8m1V1XVyeXG+8q0M4xf2NSqbShocHRsViGeVRYW1vLYrVm73UHYiKvqamhKMrRsViG2Xi0FX/UDqdSqfR6vZNGrtPpHB75L1crt5wp0j/4TM+d7/Lh5G7houZ/JVQqFU3TDo+8FTQaDUVRzhi5Vqt10tdcr9dbOoaiFU+YLYL+IQAAAECHZ1kCkemtenl5NXvU29vbBhE9qlgsFp/Pt+g1pGm6vr7ezc3N3d3dfoHZg06na2ho8PDwcHV1dXQsllGr1TKZzMPDw+lWglcqlQqFwtPTk8PhODoWy8jlcoVCIRKJ2Gy2o2OxTH19vUqlcsY3RolEotPpnDHympoamqYdG/melIJNyYVGhWIP3vrZsd38PR5Wq7KyUq/XO+NrXl5ezmKxnDFylUrF5XKdMXK5XO7q6mrRw3B7r92D/iEAAABAh2dZAhHsiqIoi0aHMQMKLK3VHjhv5EzALBbL6SJnhu8h8rbUGLmjA7EYIm8dmib/d+J24oUCo/IgL9ev5vUP8ja1sbJTv+bO+GbOQOQADof5zgAAAM4CCUQAAABr6fX06qPXj14uMSrvHijcMPcxLzeuQ6ICAAAAAACwCSQQAQAArKLR6j84cCX5ZrlReWyI1xfP93Pn418tAAAAAAA4N3yqAQAAaD2FWvd2UmZ6nvEGEXGRvqtm9OFxnGztTgAAAAAAgKZak0BMTU1tdteO1NRU5otvv/32YXXj4+Nb0SIAAEA7VNOgfmPPpTuldUblE/p0en9KtAvbybYOB7AG+ocAAAAAHVhrEog//vjjjz/+aOKEBQsWPOwQOogAANAxlEkUS3ZdKqyWGZVPH9TlHxN7sChkD+HRgv4hAAAAQAeGKcwAAAAWK6iULdmVUVGnNCqfG9f11bGRDgkJAAAAAADATixLIO7fv99OcQAAADiLGyXSpXsuS+Rqw0KKIq+P7zFrSIijogJwFPQPAQAAADo8yxKI06ZNs1McAAAATiEjv+btpEy5SmtYyGGzPnwmZkx0gKOiAnAg9A8BAAAAOjxMYQYAADDXmVsVH+zPVmv1hoV8DnvNrL6Dw8WOigoAAAAAAMCukEAEAAAwy8+ZJZ/9dF2npw0LPVw5Xz7fr3cXkaOiAgAAAAAAsDckEAEAAFq2+3z+pt/u0A8kD4mPO2/D3MciAjwcFBQAAAAAAEBbQAIRAADAFJomG0/e2ZOSb1Teycv1q3n9g70FDokKAAAAAACgzSCBCAAA8FB6Pb3m6I2fLhcblXfzc//3vP5iD55DogIAAAAAAGhLSCACANgeTZNamaaiXhOo0fE5bEeHA62k0er/efDK6RvlRuVRwZ7r5zwmdOU4JCoAAAAAAIA2hgQiAIAtnbtd8XPm/T9yqxRqHSGEkPxgb8GInn4zB4f4CfkODg4soVDrlu/NTMutNiofFun72Yw+POSFAQCa4+FhwbKw+fn5Pj4+QqHQfvHYj1MHX1BQ4OXl5enp6ehAAADAaSCBCABgGxV1yg8PXMm8V2tUXlwj/yGl4EBaUfzIbnOGdaUoh0QHlqlTaP6x59L1YqlR+fjegR88HePCxk8RAAAAAAAeIUggAgDYQEGl7O8706vqVQ87QaXRbTx5p6Cy4f2nY5BDbOfKpMrXd2Xcq5IZlU8b2GXpkz1Y+PkBAAAAAMAjhuXoAAAAnF69QvNm4mUT2cNGP2fd/+5MbhuEBK12r0q2cPsfTbOHc+O6vjWpJ7KHAAAAAADwCEICEQDAWt8m55bUyM08ecfZ3MJq4+QUtBM379ct/C6tXKo0LKQo8vqEHq+OjXRUVAAAAAAAAI6FBCIAgFUkcvWhjCLzz9fq6F3n8u0XD7Ta5YKav3+fLpGpDQvZLOr9KdHPDQlxVFQAAAAAAAAOhzUQAQCsknKnSqPVW1Tl7K0KnZ5mszAZth05faP8nwevGP0o+Rz26pl9h0SIHRUVAAAAAABAe4ARiAAAVrlZYrxRb4vqFJpis6c8Qxv46VLxe/uzjbKHHq6c/3uhP7KHAAAAAAAASCACAFjFnL1TbFUL7GH3+fzVR6/r9bRhodiDt3n+gJjOIkdFBQAAAAAA0H5gCjMAgFV4nNY8ieG64PmN49E02fTbnd3njZekDBS5fjWvf2cfgUOiAgAAAAAAaG+QQAQAsIqfkN+KWgGerakFNqTX06uPXj96ucSoPNzfY8Pcx8QePIdEBQAAAAAA0A5hCAwAgFUGhPlYWiXU1823VWlHsBWNTv/+/uym2cNeQZ4bXxyA7CEAAAAAAIAhJBABAKwSG+pl6SDECb072SkYMIdcpf3H7ku/3yg3Kh8SId40f4CngOOQqAAAAAAAANotJBABAKzCYbPmj+hmUZVq7KDiOHUKzZJdlzLya4zKx8UEfvFcPz6H7ZCoAAAAAAAA2jOsgQgAYK3rxRKLzt+fVujiwnp9fHc7xQMPU1Wven33pdzyeqPyZwd2efPJHiyKckhUAAAAAAAA7RwSiAAAVtl9Pv9YpvFSek3QhDyQnEq6UEAIQQ6xLd2rkr2+K6NMqjQqnxvX9dWxkQ4JCQAAAAAAwCkggQgA0Hp/5FZvPnX3gSKKELrpiZSXG7dWpjYsSrpQQBGyBDnENnHrft0bey5JHvwRUBRZMq77c0NDHRQUAAAAAACAc0ACEQCgle5Vyd7/MUuvfyBfOHtoaGSA8GJOVVFVQ4NCFejjEe7vMbKnf/dA4Xs/Zp25VWF4cuKFAooir41DDtG+Mgtq3krMlKm0hoUsFvXu36ImxwY5KioAAAAAAABngQQiAEBryFTaFXuz6pUP5KQGh4tfHRPJYlHjewfW1dVVV1d37dq18eiqGX2b5hB/SCkgBDlEOzp/u/K9/dkqjc6wkOPC+mRa75E9/R0VFQAAAAAAgBNBAhEAwGJ6mv7ngSv5lQ2GhSFit0+n92GxHroRhwubelgOkSLU38dhGb5WKqiU/XatNCO/prRWrtbqAkRFUcGiUb38H+vq/Wv2/U+PXNM9OErUg+/yxfP9+oZ4OSpgAAAAAAAA54IEIgCAxb4+cSflTqVhiYcr58vZ/dz5LbypurCpT2f0ee/H7LMP5hD3pOQTQpBDtFS9QrPhv7d/zbqvp/+XIqyV1928X3cgrTDYy/W+RGl4iBDi7c7dMLd/ZIBHmwcLAAAAAADgrFiODgAAwMkcz76feKHAsITFolY+2zvYW2BOdQ6btWpGn8d7+BmV70nJ33jyjq2CfBSU1Cpe/uaPnzNLjFKEjYprFUaHAkWuW18ahOwhAAAAAACARZBABACwwO3SujVHbxgVvj6++5AIsfkXYXKIw7sb5xB3n0cO0Vz1Su3SPZcKq2XmV+nq67715YGdfczK8wIAAAAAAEAjJBABAMxV3aBalpipfHA7jif7dpo5OMTSS3HYrM9mNp9D/PZ0jlVRPho2nbxzr8qC7GGvIM/NLw3wE/LtFxIAAAAAAEBHhQQiAIBZ1Fr98qSsijqlYWFMZ9E7T0W17oJMDjGuu69R+bfJud8m57YyykdDSa3ip8vFFlWZ0i9IJODaKR4AAAAAAICODQlEAACzfH70+rViiWGJr5C/emZfjkvr30g5bNbqGX2bTn/+9nTOduQQH+7362VGGyu3KPnBXWsAAAAAAADAfEggAgC0LCn13s9Z9w1LeBz257P6ij14Vl6Z48L6fFbs4HDjHOI3p3O+O4McYvMuF9RYWiWzoPYhW60AAAAAAABAC1wcHQDYy/jPf5fKNeaf37uLaNvLg+wXT9t7Z1/W6RvlhJB9r8WFiN0cHQ44sbTc6q9P3DYqfPdvUb2CPG1yfa4L64vnY9/Zm3X+TqVh+bbfcwghL43oZpNWOpLKOpWlVZQaXZ1C4yng2CMeAAAAAACAjg0JRAAAU4qq5e/tzzaaMDs3ruv43oE2bIXDZn02s+/bSZkXc6oMy7f9nsOiqBcfD7NhWx2AvlWDCVtXCwDap1u3bh04cKCwsFAikQQFBfXq1WvWrFkeHh5Gp0ml0qSkpLS0NIlE4uPjM2TIkFmzZgkEZu3Gbk3dRkqlMj4+ftiwYQkJCRZVBAAAAGhXkEDs+EQCrjlrtHlhewGAJmQq7bKky/WKBwbzDokQJ4yOsHlbXBfW2udilyVe/iO32rB8y6m7hBDkEA2JPXh5FQ0WVeGwWRh+CNBhHD169Ntvv6VpmsVieXh45Obm5ubmnj17ds2aNUFBQY2nVVdXL1u2rKqqihDC5/PLy8uPHDmSkZGxdu1ad3d3002YWTc7O3vv3r15eXlisTguLm7atGkczgNvNUePHlUoFDNmzLDl/QMAAAC0OSQQO77PZvbpF+rt6CgAnI+epj88cKWgUmZYGCJ2+2RaHxaLskeLf+YQkzLTmuQQKYq8MBw5xD+F+boZvUQtiu4sYlF2+akBQBurrq7euXMnIeSll16aNGkSh8OpqKj48ssvb968uWnTpk8//ZT6649927ZtVVVVYWFhb775ZnBwcF5e3urVq4uLi3ft2rV48WLTrZhTNzk5ed26dXw+PyIiory8PCkp6ebNmx9//HFjADKZ7NChQ08++aSPj4/dXg8AAACAtuAEm6jcunXr008/feWVV2bMmPGPf/zjm2++qa+vb3qaVCrdsmXLSy+99MwzzyxYsOC7776Ty+VmNmFN3UZKpXLOnDmbN2+2tCIAtE+bf7trtCihG89lzay+7nw7PnrhcdhfPt9vaIRv02C+P5tnv3adyKnrZUcf3NDGHKOj/O0RDAC0vZ9//lmtVg8dOvTpp59mhvv5+fm9+eabbDb76tWrzJhBQohUKk1LS+Nyue+8807nzp0piurWrdu7775LCDl79qxarTbRhDl11Wr11q1bAwICtmzZsmrVqq1bt06cODErK+vChQuN1zly5IhWq502bZr9Xg0AAACAttHeE4hHjx5dvnx5WlpaRUUFj8fLzc09evTo4sWLS0pKDE+rrq7+xz/+8csvv1RVVbm4uDDTTN56662GhpbnuJlZNzs7+5133pk5c+arr76alJSk0RjvT4IpKgAdye83yvek5BuWsChq5bTeXX1bmPVmPY4La/XMPgPCjIerbDn1qOcQ65XaDw9eee/HbJlSa1HFAE/+3/oF2ykqAGhjhYWFhJChQ4caFvr5+TGTl4uKipiSc+fO6XS63r17+/v/7/lBWFhYWFiYXC7PyMgw0YQ5dQsKCmQy2dSpU729vQkhbDb7hRdeIIRcu3aNOb+uru4///nPU0895elpmx23AAAAAByoXScQDaeo7N+/f/fu3d9++23Pnj2lUummTZtog+XwG6eZbNy4cd++fevXr/fz82OmmbTYijl1k5OTP/jgg9zc3PDwcJVKlZSU9MknnxgGgCkqAB3JndK6lYeuGm258fdxkcMijQcG2gmPw/7X87HIIRr6I7f6+Y0p/71SamlFNot6d0o014ylYAHAKUilUoFAYJjaY/B4PEKIUqlkvr169SohJDY21ui0fv36EUKys7NNNNHquhRF6XQ65utDhw6xWKypU6e2cD8AAAAAzqBdr4HITFEZNmzY008/zZQwU1QWLlzITFHx9fUlD04zYXqTzDSTN9544+zZs/Hx8VzuQ7cHMadu4xSVNWvWeHt763S6bdu2/frrrxcuXBg2bBhznQ48RUWvp68WSwoqZRK5WuzBD/JyjQryNGdXFkZuef31EmmtTO3pyo3u7Bnu/8D2iGVS5aW86uoGtdCVEyIWRAWLTH/IL66RF1XLy6VKqULt487zE/LD/NzFHjxH3R10SDUN6mVJmUqNzrBwQp9Ozw8NbcsweBz2F8/HLkvMTM971NdDVGl0G3+7u/+Pe013UaYoija5tzKbRa14KmpgNzzaAeg4vvjii6aF9fX1xcXFhJCQkBCmpLa2lhDSqVMnozMDAwMJIRKJxEQT5tQNDQ0VCASHDx8eOHCgj4+PTqfbuXMnTdMxMTHMFY4dO/bss8823RgaAAAAwBm16wSiiSkqhYWFRUVFTAKRmWYSGxvbdJpJXl5eRkaG0RUMmVOXmaIyb948wykqv/7667Vr15gEYkedoqLV0QfTC3eczZPIHlgnyNud++yALs8PDXXlso2q/OvnmwfSCgkhKR+Oq6pXfXzo6uWCGsMThkSIVz7bW8BlNah03x69fepGheHRIG/Biqd6NR11RQi5XFCz82zeH022TWCxqCd6+b/4eJhRatIedwePAq2Ofu/HrHKp0rCwe6BwxVO92j4YPof9xfOxb/1wOSP/gb+jzb/dJeRRySHeKJF+dPBqYbWs6aGoYM/FYyK2J+cZvc80CvIWvPO3qP5dsYsUQIel0Wjq6+vz8/P37dunUCiGDx/euAuzVColhLi5uRlVYfZQZo4+jDl1uVzuokWL1q1bl5CQwGyiUlFR0a9fv7i4OELI/v37uVzulClTbHKbAAAAAA7XrhOI1k9RycvLy87ONpFAbHXdDj9FRaHWvZ1kPPSJUdOg/uZ0zvnbFV/OfszbvfnRnSW18le2pxnl5gghqXerliVlfvJszEe/FpVIjI+W1MiXJWbu/fuwAJGrYfmRjOI1R68325BeT/92rSzlTuWWlwZ2DxS2zd1BB/bFzzcy79Ualvi48754PpbPcUxCmc9h/2t2v2ZziBSh5g3v6pCo2oZOTydeKNj6+12tzniMIZtFPT80dOETES5s6rGuPhdzqk5cLc3Iq6mqV+lp2lPAiQoSjerlP6FPIIeNAcUAHdlbb72Vn//nerUTJkyIj49vPMSk+ZiUnyHzE4gt1h05cqRIJNq7d29OTo6Pj89zzz03ffp0iqIqKyuPHz/+/PPPCwQCM2+krq4uNTW1aTlN01Kp9N69e6ar19TUMKMmnRGCdwiapmtra00PxW23ELyjOHvwEonE9Jt/u6XX6xG8Q3Ts4E1P5GpWu04gYoqKo9A0+eeBK435tfG9A/uGeAV7C4qq5el51advlBNCbt6vW7Ir4/uFQ1zYVNMrLEvMlMjUcd19n+7fOczXvaRW/v3ZvEv5NYSQrHu1c7akSuUasQfvlt+QUQAAIABJREFUlVHhvbt4KTW6U9fLfkgp0NO0UqP77kzeu1OiGi9VJlGs+/Um83WQl+vT/TuH+roJuC5V9apL+TXHr9xXa/UKtW7zb3c3zH2sbe4OOqp9F+/951KxYQnXhfX5c339hHxHhUQenkPc9NsdQkhHzSHeq5J9dPDKzft1TQ+F+rp99EzvHp3+98BgcLh4cLiYEFJRWSWTK7qGdG67QAHAoUJCQmiaLi8vVygU586dCwsLmzBhAnPIdLe46W54hsyv27dv3759+xqd8OOPP7q5uU2ePLmF6A3weLzw8PCm5bm5uXw+XyQSmahbU1Pj6urK5zvyX1WrOXvwAoGAGdngdGpra533lXfq4J39d955g6+treXz+a6uri2f2v4geEfp2MHr9XpLr9muE4iGMEWl1d7dl93ion7fxg/y9/zff4KT10rP3a4ghIjcuF88FxvT+c9u64Awn2cGdL6YU/X+/uwGpTanvH5/WuFzQ0KaXvBelWze8K4JoyMpihBCOnm5Phbq/cLW1Ltl9YQQqVzT2Yv31dzYQJ8/J3336CQUcNlbf88hhNwteyBlcPxKqVqrJ4REBgq3vjTQcGbx+N6BE/sELv4+nabJ9WIJTRPKjHSf9XcHHVJ6XvVX/71tVPjWpJ7RwaY+trUNJof45g+XLzXJIVIUmRvXoXKINE3+c6n43/+9pVDrjA5RFJnyWPAbE3o8bEAom0VxkPQHeJQsXbqUEELT9JkzZ7766qtNmzZ5eXkNGjSIECISiRQKhUxmvABCQ0MDIcTLy8vEZa2pW1ZWdvLkyZdeeon5jC2Xy3fv3p2RkSGXyyMjI+fNm9e1azNv2jwer1u3bk3L8/PzeTye6UVymI/0QqG58zDaFWcPns/nO2nwzAdLJ11/CcE7ilMHL5FIELxDIHhHaTH4jpxA7DBTVLRabbODImma1mq1CoXCzOuQvx6PazSaZms1PjyXyI1nCjclVygUXPqvimT3uT+3eV3+ZGS4mGd0/T5Bbq+N6bb62G1CyJ7zeVP6+DWm7bRaLfNFqNht7uBgpfKBipP7BKwvq2e+XjDUz5VNG155cm8/JoFYVC0zLL9W+GfGZM6QYKJTG91rD3/XMF+33ApZvVJbUiX1MZh03DjHXKlUKhQs6++u8QZVKlUrhvs6lkqlIoQolcrGn5GzUKvVhBClUsli2XEuaplU+f6P2Tr9Az/WGQODx/b0seiv0hAzRKXV1Zv69Jme7x64nnnvgTeQjSfvaDSa5wbbcsydVquladqGkZuvRqb+4pc7F3ObWdPQ24379qTIQWHetFateMhvsQMjt5JOp9Pr9c4YuV6vd9LXXK/XW/pvt51oRYehFR1E50JR1MiRIwsLCw8cOHD8+PHGBGJpaWnTJCBT0mICsdV1k5KSRCIRMxBSpVItW7asqKioc+fO3t7eWVlZWVlZq1ev7tGjh+V3CQAAAOBITpNA7DBTVGQy2cWLF5uWczichoaGsrIy8y/FkEqlzSZJLcpwVVZWEgWH+fq+VH23vIEQ0k3MD3VTNxtSjA8JEHLK6jTVDeorOUX+Hn/WlcvlzBeDurhWVZYb1XKl//y008mTG+Hr2nTJGHceu0Gl0+ppw0ZHdXPtH9yJENJFoGk2GFf2n3daXlGhafjfr3TjKplVVVVcDdf6u2tUU9P8jg3tX1VVlaNDaKWKioqWT2otlZb+4Ng9qeKB94qYToIpPQWt+JM0Yv0VDL0+XLxWpb5RJjcs/OZMvkreMKGXqQ+0rWDbyM1xsaD+u4sV9UrjgYeEkMGhHi8P8XPnNf83a6TtI7cVRN7GnDSBSAhpaGhghsKZyekeeplQW1t76tQpd3f3xn5go169ehGD/xfMtN/S0lKj08rLyxuPPkyr6xYVFSUnJy9atIjL5RJCDh8+XFRUFB8f/9RTT1EUVVBQsHz58i1btmzYsKHlWwUAAABoT5wmgdhhpqi4u7sPHz68aXlaWpqHh0fT1RhNoGm6tLRUJBI1O/6Rov4cZ/fvOX37drFgDmZW5Z+fAwdF+JmIJ6aLtOxaOSGkRsuP7fTnRjdubjJCJISQPt0COnUSG1Wp1ksJKSaERAZ6EkK8vb2NVtDgc+81qHQURRm2a/olqZVpCiV/3qm/v7+vx/+WoXF1rSWkgRDi5+fXyUdg/d0RQjQaTWVlpVgsZj4YOBGVSlVdXe3n5+fi4jR/9QyFQlFbWxsQEGCnEYg0TT46fL3owS19OnsLVs+M9XA1zh1bRCaTSaVSi/6ozbFuTuCKH69mFf5vHCJNk13plSKR6Jn+QTZpor6+XiaTBQQE2ORq5mhQav994u6Ja8ZPHQgh7nyXN8ZFjI023k2rWVKpVKVS+fn52TpAu6utrdXpdGKx8dtm+1ddXc1isUz/u2yfKisrORyO6SxS+1RWVubm5mbRyst6vf7GjRv2C6ktcbncXbt2ubi4jB07ls1+YDWD+vp6Qoi3958br8fExKSmpmZnZ0+aNMnwtKysLEJIdHS0iVZaXTcxMdHX13fs2LHMt1evXhWLxUz2kBASGho6ZsyYo0ePyuVy8yevAAAAALQHTpZK6ABTVNhsdrOz0CmKYrPZFi3DzAwocHFxabYW9dfMWw6HY9Fl70tVzBeJqYWJqYUtnq/UUY3Xb+zKi9xdmzbK4fyZjvFw5TYbWGPMJgLW6+kyqbKoRp5bXv9HTlXmvVpmhURCCJfLNazYmG8yLLfm7hojtPQlbQ+Y+WtcLrfxp+AsmDnXXC7X6IOirWw5dTf5VqVhiYDn8vlzsWKR8coGlmKmjdv8V4XHI+vn9n/zh8uXC/43EpamyVcn77q4uEwf1MX6JuRyOUVRbfZLnp5X/cnhaxV1yqaHBoT5fDA12vxNbNhsdltGbkMsFoumaSeNnMViOWPkrfi32060IvKONIXZzc0tICCgrKzsjz/+GDp0aGM5TdMpKSmEkLCwMKZk+PDh27dvz87OlkqljV2vsrKyO3fuCASCwYMHm2ildXXz8vJSUlKWLFli+Kyu2Re/I/1EAADg/9m787gqy/z/4/d9zuEczjnsBxAQUFFxQVTUNJdSs02tKc3dsZr52WJN21TfyWmamWoqyxazmjabGbNR0xmzXUdL3ItMRcAFFUH2HQ6chbPdvz9OER4W2c858Ho++oOu+7oOnxtR4M11Xx+gl+jCM8U6qLKy0pkSNr7kOY+oLFy40OURlTfffPPFF1987bXXlErlO++8c5mb9Eh6U0sPfTdmqOuOM/VOXKx65atTi948cPXfds1ds++hD4+s3Xnm+/Pl9elhK3nm3cFdkk8Vr//5TEwnmSg+M29kXHhH08MupVbKX1k6Jql/SMNBSRJe/frU1u8vH4t7DovN8dauzIc+/LFxeqjykd9/Xfzrt491bwtsAB7o5ptvFgTh7bffTklJcZ53rNfr33///ZSUFLVaXb9nMDAwcPz48SaT6eWXX3YesVJdXb1q1SpJkqZPn97wSYKtW7du2rQpIyOjfqT1axvauHFjVFTU9OnT60cSExMrKiq2bdvm/KVvVlbW7t274+LiGp++DQAA4OE8dwcij6i4S30fialDwwdHXP4JqeF9u7Ynkclif3pbWvKpX55t9FHIYnXaWJ2mX6h2/EDdP/dm/ZBV3spX87S7gxudLap5eluay8lgK64dPCU+zE0VtYFaKX916Zjf//vosUv3Ib769SlBEDplH2JXO1dc89f/pp0rrml8aXjfwL/MTewXqu3+qgB4vtmzZ3///fcnTpz429/+JpfLtVqtXq8XBEGlUj3wwAMNDzG4++67MzMzU1NTly5d2rdv39zcXEmSYmNjly1b1vAFt27d6mzVlZCQ0Na19TIzM1NSUh577LGG37XOmTNn//7969ev37lzp7+///nz50VRvO+++zr5IwIAAND1PDdA5BEVd6k/921Y38A7r45zbzGCIDy1NfVAZqkgCOEBvvMnxE4YqIsL91fIf2mN/OH+C61/NU+7O7hLtdH6xOZjJssl/TquGd7n15ObOLrUM/2UIX7047GcX/oROTNEURTmjffcDNHukDYeyn7v23NWu+u/kHKZuGRS/7uvGeQj99wN8gDcSyaTPfvss7t27dqzZ09hYaHRaIyLixs8ePCCBQvCwi75DZBOp1uzZs3GjRtTUlLy8/PDwsKmTJmycOFCtVp92ffS1rUfffRRbGzslClTGg6qVKrVq1c7z8guLCxMSkq68847+/Xr15HbBwAAcAvPDRAFQbj55pvff//9t99+W6FQjB07Vi6X6/X6zZs3N/mIyuHDh19++eWVK1dqNJoWHlGx2WwjR46s/w1z69c21OQjKmlpadu2bZs7d64oil79iEpE4E8PDGaXuR4N2f3S86qc6WGsTrvurgkBTTW1qGvLU8wedXdwF5tdWvnx8fzKS7qvxkcG/Hluoig2t8gTqZXyV389tnGG+MpXpwTBQzPEgkrT09vSUi+6NmEXBKFvsPrPcxNHxXpfOw4A3UwUxeuvv/7666+/7MzAwMAVK1asWLGihTlbtmxp99p6zzzzTJPjGo3mnnvuueeee1rzIgAAAB7LowNEHlFxi8SYn05+PJ5TabU5fBRN7AOSJOH5z9KrjVa5THzmtpFNzukUx3+ORa5PjGgyPbTaHCfzq1v/gh51d3CXV7461bADiSAIIX7K1YuTfH26pE9Ll1Ir5S8vHfPQhz+m513Sl/mVr06JgnCbh2WIXx0vWP3lSZeNn4IgiKJwy9joh24YqlZ63x8BAAAAAPR4Hp2MOB9R+d3vfpeQkBAYGGixWOLi4m644Ya///3vLk+IOB8zmTlzZlBQkPMxk7lz565evbo15w+2dW0Lj6jMnj1bkiTnIypr1qyJj4/vyO27y5DIAGfXgqIq09aUphsyHDxb+vnR/H2nS8xWe5fma7Xmn3qY2BxSkxM+OnihzuoaRrTAo+4ObvGflIufHMltOKKQi88tGN0n0FubdWhVijXLxiZEX3JepyQJL3916r/NfJJ3v4pay2Mbjz7zSVrj9DDET7l6yZgnbk4gPQQAAAAAz+TROxAFHlFxB7lMnD8h9q1dmYIgvL/nXFSwetqwPg0n5JQZnv803fn2zFFRXVpMfTPcHakF88bHhvqr6i/pTdYNBy5sOPDLAYi55cbLNmz1qLtD90u9WPn6jjMug4/PHp7Uz7sfm/XzVby+bNxDG45k5P2yIdeZIYqiOPeKGDfWJgjCnpPFL35+sspoaXzpmuF9/nBzQqCmif3FAAAAAAAP4ekBItxi0cR+u9KLMgv1Jov9ic3Hpw3rMy4upF+otsZsO5Zd8fnRfLPVLgjC1KHh146I6NJKEmOCFHLRZpeKqs3z1+6fOqxPZJC6zmrPLTceuVDu3MoUFawuqDQJgvDs9vQZCX3mjIuJDmlp56nn3B26WVGV6Q+bj7s07lh4Zb9bxka7q6RO1FyGuPrLk4IguCtDNNTZ3vjfme1H8hpf0qoUD1w/5NZxPeGDDwAAAAA9GwEimuAjl72yJOnBD3+8UForCELyqeLkU8Uuc4ZFBfz1tpGyLm45ERmkXjEj/o3/nREEwWSx70gtaHhVJoqPzByaEB24fN33DodUVGX698HsKweFthwges7doTuZLPbHNh6rMlyyCe6KON2DNwxxV0mdzpkhPvjhkYYHg7oxQ0zLrfrrtrT8CmPjS+MGhDw1J9F7HxsHAAAAgF6FABFNCwvwXXfXhH/ty/pPykWXM8uCtMq7pg28ZWyMQt4d+driSf3USvn7e85VXhr9jO4X/PCNQ4dGBQiCsGrh6L/vziyqMsfoNIFN9Vpx4Tl3h+4hScLfPk0/V1zTcDAySP3s/JFyWY/6g/bzVay93f0ZosXmeH/PuX8fzHZIrqeXKhWyu6YPWjq5PwE9AAAAAHgLAsQea+cfrungK2hVivuvi79r+qCj2RX5FcbaOltkkLpfqLZ/qFbVTLPax2YPe2z2sOZecFRs8HdP3yAIgt1uv3ixid4Onz86tfGgTBTnXhEzc1TU6YLq3ApjlcEaFqAaGRvcN1hdP+fqoeFXDw13WfjCwtGde3fwXh8kn/smvajhiLN5cZBG6a6Suk4LGaIoCnPGdXmGeL6k9un/nsgsqml8aWAf/7/OTRwc4d/VNQAAAAAAOhEBIi5DqZBdOSjU3VUIaqU8qX9IUv+Qzn1ZD7k7dKm9p0v+sTer4YgoCk/NSRz4c4uensfPV/H67eMeapQhvvTFSUHowgzRIUlbv7/45q5Mq83hckkmivMmxD5wfbyPnM7mAAAAAOBlCBAB9GQXSmuf2Zbm8iDtPdcMvmZ4n+aW9Az+zWSIq784pVTIZ4/u/A7jhVWmZ7alHcupbHwpMkj957mJ3t7qGgAAAAB6LXaCAOixqo3WxzYeM9TZGg5OG9bnjqvi3FVSd3JmiMOiAhoOOiTpue3pXx0vaG5V+3x1vGDp3w81mR7OHBW18f7JpIcAAAAA4L3YgQigZ7LZpT9uOe7SAjg+wv8vcxN7T/cOf1/F2juueHD9D6cK9PWDDkn626fpgiDM6ox9iJUGywufZew7XdL4UrBWufJXCY3PJwUAAAAAeBd2IALomV79+tSPFyoajgRqfFYtSlIre1eTHGeG6LoP0SH97dP0r1M7ug/x8NmyZW8fajI9nDg4dMOKSaSHAAAAANADsAMRQA/0xbH8bT/kNhxRyMVVC0dHNWje3Xv4+yrW3j7ugQ+PnG64D9EhPbs9XRCEmaPasw/RWGdb+78z24/kNb6kVSkeuH7IreOi210wAAAAAMCjsAMRQE9z4mLVi5+fdBl8dNawTu/i7UX81T5v3D5uaKN9iM9uT9/R9n2IablVt79zuMn0MDEmaP29E0kPAQAAAKAnIUAE0KMUVZv/sPmY1e5oODhvfOyccTHuKslDNJchPtOWDNFml9btOXfPP1LyLj1cUhAEhVxcPm3gu78dHx2i6ZyKAQAAAACegUeYAfQcdVb7ys3HKg2WhoOjYoMfunGIu0ryKP5qnzW/Hnv/+iPni2vqB50ZoiAIQ6MCd5woSDlfXlRlrLPawwIvDokMmD68z9VDwmUyURCErJLav25LyyzUN37luHC/v85NjI8MaHwJAAAAAODtCBAB9BCSJPzt0/SG7YYFQYgIUr+4aLSPnN3WPwnSKt+6Y1zjDPHpT9JEQXRIUv2godSQXWrYeaJwYLjf4zcNP1Oof3NXptXmcHlBURTmT+j3wHXxPgo+yAAAAADQMxEgAugh/rXv/K60ooYjaqX85SVJQVqlu0ryTE1miJIkSILU5PzzJbUr/pkiNXUxIkj95zkjxvTiwyUBAAAAoDdgwwiAnmDf6ZL395xvOCKKwp9uGTGoj7+7SvJkzgxxYLhfK+c3mR7OSIjYcO9E0kMAAAAA6PEIEAF4vexSwzOfpDkuTbmWTxs0Y0SEu0ryfEFa5Zt3XhGk8Wnf2lWLRj+3YJS/uj3LAQAAAADehUeYAXg3vcn66MajtWZbw8Fpw/r8dupAd5XkRYwW1zMNL2tKfNgfbxkR4seD4QAAAADQWxAgAvBidof05JbU/Apjw8FBffz/MjdRFN1VlNfYf7rEYrO3aUlEoPrlpWO6qB4AAAAAgGfiEWYAXuz1nWd+yCpvOBKkUb60OEmtlLurJC9yLKeyrUuK9SZjne3y8wAAAAAAPQgBIgBv9fnR/C3f5TQcUcjF5xeOigpWu6sk71KqN7d1iSQJpTV1XVEMAAAAAMBjESAC8EppuVUvfXnSZfD3M4fRFLj1mmqt3IpV7VsGAAAAAPBaBIgAvE+J3vzE5uNW2yUNQG4dFz33ihh3leSNQv1V3bYKAAAAAOC9CBABeJk6q/3/Nh0rr73kQdrR/YIfnTXMXSV5qVGxwW1dMqiPv58v3bcAAAAAoHchQATgTSRJeP6zjNMF+oaDEYG+Lywc7SPnH7S2uXpoeFs/aNeOiOiiYgAAAAAAHouftwF4rvwK49GcqpSc2hO5VVUGiyAIHx7I2nmisOEclY/8hUVJwVqlm2r0YqH+qjY99B2kVc6fENt19QAAAAAAPBNPogHwOBab4z8pFz/9MS+nzPDzWIFMJvYP1V74ZUQQBEEUhaduHTEsKqD7i+wZ7po+6PvzZdmlhhZnSYIgykTxj79K0Kr4qgEAAAAAvQ47EAF4lnPFNYvePLB255mcS7JCyeGQskpqJcclPYB/c/VAHqrtCD9fxStLxvQN0bQ4S5TJxEdmDr16aHg3lQUAAAAA8CQEiAA8SEZe9V3rvi+oNDW6IjaefPXQ8OXTB3ZDVT1b3xDNP+66ckZChNjEx1gQBKFvsPrVpWN4eBkAAAAAei0eRgN6izqbw90lXEZFreUPm4+ZLPbWTFYr5X+ekyhrLvRCWwRqfJ5bMOpkfv+vUwtSzpcXVplsdinETzk0KmDasD43jIykQQ0AAAAA9GYEiEBPlldh/PTHvEOZpbkVRovN4etzYUCYdvKQ8FvHRof6q9xdnat1yefKaupaOdlksR/NruCh2k40vG/g8L6BgiBUVlbq9TX9+rHlEAAAAAAgCASIQE9ltTve2pX5n5SLNvsvhwaarfZTBfpTBfqPDly48+q4O66K85wNfDVm2+dH89u05KODFwgQu4jnfGIAAAAAANyOABHogUwW++8/+vFYTmVzE8xW+zvfnM0s1D87f5Rc5rasyGpz6M1WvcmqN1n3niq12tv2kHV6bnWlwRKsVXZReQAAAAAAQCBABHoehyT99b8nWkgP6317sli34/Sjs4Z1bgF1VnuN2aY3WWvM1hpT/RtWvdlWY3K+Ya0x2WrM1vLaOkm6/As2xyFJF0prg7UhnVc7AAAAAABwRYAI9DS704v2ni5p5eSt31+8bkTkyNiglqfZHZJzn2D9fzXON8w2vcmqN1przFa9yVptstaYrHZHB0LBNipv9ZmJAAAAAACgfQgQgZ7m/T3n2jR/7c4zD9wQ3/JWwYpai6MjewW7jL/ax90lAAAAAADQwxEgAj3K2aKa3HJjm5ak51Xd80FKF9XT1aKC1e4uAQAAAACAHk7m7gIAdKYTFy9/9KGnUSpkof6qGJ2mqYstbXuMDtHE6rRdVBUAAAAAAHBiByLQo5TVWtxdwk/8fBUBah/nf/5qn0DnG74+AWqfAI1PwM9X/dU+vj5y55Lf/euHIxcqLn2ZljpE3zI2usvKBwAAAAAAPyFABHoUeUuBWydQKmTOHNBfrQjz99X5qwJ8Ff4/J4P+aoXzjSCNUtH2Un53/ZD/9/53rezBEhmkXjAhtu13AAAAAAAA2oYAEehRwgJ827EqWKsM81c59wPWbxsM/Pl//X/+X7VS3ukFNzQ0KuDhG4e+8tWpy85UK+UvLhqt8unaegAAAAAAgECACPQwY/qHtHWJj0L2n4eu0qo84l+D+RNiBUFYs+N0C/sQQ/yULy5Kio8M6Ma6AABwD0mSHA5Hx+d4LIp3F4p3F4p3F4p3F4p3l5aLb8d9eURkAKCzxOg0Q6MCThfoW79k8uAwD0kPneZPiB0RE/TW/840Og9RUMjFm5Oil08fqPNTuaU2AAC6k8PhMJvNFRWuXxBdGAwGq9XaPSV1OqPRSPFuYTQabTabu6toD0mSKN4tekDxdrvd3YW0hyRJJpOJ4rtfzy5eklp1dFhDHpQaAOgU90wf9Mi/j7Zyskwm3nXNoC6tpx2GRQW8eecV+RXGQ2fLLhRXl+sNMeHBg/r4T4oPC1D7uLs6AAC6iUwmU6vVoaGhLcypqanx8/MLCPDKjfk1NTVarZbiu19tba1Wqw0MDHR3Ie1B8e7i1cUbDAatVhsUFOTuQtrDYDBoNBqK7349u3h2IPYKJot9x4mCvadKzhXXVNTWaVVZkcGaSYNDZ42OitVp3V0d3O/w+fLWT14xY/DAcL+uK6Yj+oZo5k+INRgMJSUlsbGxcjknHgIAAAAA4AYEiF5mV1rh6zvPlNXU1Y/UmG01hfrMQv2GAxduHRfz4PXxdJbozT47mrflu5xWTl46uf+yKQO6tB4AAAAAAODtCBA9iMPhaPlEiXXJWf/af0EQBEGQBEF0uWp3SP9NuXgyr2r14lFBGmWXldkJnM/h2+12rztBw8MrP36xavUXl29hLAhCeIDvfTMGXjsiwjNvpCHnx9xms7XjjAb3cu4J9/yPcGNeXbkkSd5YuSRJVN7NnKdKe2PlQiu+YWg8v+uKAQAAQG9AgOgpHA6HxWLR65vtffHliZJ/7a/fWeaaHtY7VaBfufn4c3OGKOTNznE7ZxJkMpm87thpZ5hlMBjq6uouO7mb5VWan9hyymq/5KfE6UN1CVF+P1yozq8yVRttIX7KmGDfCXHBVw0OVipkLXy+eQ7nJ0lNTY1MJnN3LW1jsVgEQfCKD7KLuro6SZK8sXKLxeJwOLyxcqvV6qUfc5vNJoqiN1bucDisVquXVl5XV9emyr3uFzAAAADwNASInkImk/n6+oaEhDR5tVRvXncgr5UvlZZf8+252kUT+3VedZ3Mbrfr9Xo/Pz+NRuPuWtrGYrHU1tYGBAT4+vq6u5ZL6E3Wv310stZ8yYaUpH7BzywY4yOX/XqqYDKZioqKoqOjfXy8rAmJwWAwGo3BwcFedwaiXq83mUzN/aX2ZJWVlTabzRsrLy8v99KPuc1ms9vt3li5xWKRyWTeWLnJZFKpVN5YeW1trVqtDg4Obv0SdiACAACgg7xsR0+v9Y+9WXXWNvQO/+e+8xYbPy30Fja79OSW1IvlhoaDfYPVLywa7SPn7zgAAAAAAOgQwgUv4HBIe04Vt2lJtdF65EIbWvHCq7305ckfsi754/ZX+7y2bKyHH4UJAAAAAAC8AgGiF8guM1QZLG1ddTynsiuKgafZdCj7sx8vebxdIRefXzAqVqd1V0kAAAAAAKAnIUD0AmU17enXUar3uC4f6HTfnSt7Y1emy+DvZw7iNUgLAAAgAElEQVS7Ik7nlnoAAAAAAEDPQ4AIeKsLpbV/2prqcFzSW3PRxH5zr4hxV0kAAAAAAKDnIUD0AmEBqnasCtG2ZxW8RZXR8tjGYy5tl68cFPrA9UPcVRIAAAAAAOiRCBC9QD+dNljb5m4YX50o2J1eJEmXnwmvY7E5Ht94LL/C2HAwLtzvuQWj5DLRXVUBAAAAAIAeiQDRC8hk4jUJEW1dVVlb96etqf/v/e/Scqu6oiq4iyQJz3+W4fLHGqjxWb04SatSuKsqAAAAAADQUxEgeoffXB3n6yNvx8KT+dV3f/D9k1tSi6rNnV4V3OJf+87vSC1oOOIjl61aOLpviMZdJQEAAAAAgB6MANE7hPqrfj9zaPvWSpLwTUbRojcOrNtzrs5q79zC0M2STxW/v+e8y+Djs4cl9Q9xSz0AAAAAAKDHI0D0Gr8aG333NYPEyx1wNyDcb0CYX+Nxs9W+Lvn8/LUHvjpewMGIXupMof7pbWmOS//87rw67ldjo91VEgAAAAAA6PE4Mc2b/HbqwLhwv9e+Pl3c1PPIPnLZvPEx910bL5eLO1IL3/jfmUqDxWVOid78zCdp/0m5+MjMoYkxQd1SNTpHWU3d4xuPmSyX7CGdNqzP3dcMcldJAAAAAACgNyBA9DLThvWZNDjsf2mF+06XnC2qKa0xB/j6RAarJw4Omzkqqm+w2jlt1uioq4aGf7g/a/N3OVabw+VFnAcjXjM84oEbhkQE+nb7TaDN6qz2/9t0rER/SXAcHxnwl7mJssvuSgUAAAAAAOgAAkTvo1TIbkrqe1NSX0mSsrOzQ0ND/f39G0/z91Xcf138r8ZEv/PN2W8yilyuOg9GPHS2dOmk/rdfFadU8DC755Ik4dnt6SfzqxsOhvqrVi9OUivb01oHAAAAAACg9YiNergYnea5BaPeuGPcoD5NhIwmi31d8vl5r+/nYERP9s43Z3enXxIBq3zkLy1O6sPuUQAAAAAA0PUIEHuFK+J06++d+MTNCcFaZeOrzoMRl6/7Lj2vqvtrQ8u+Ti1Yvz+r4YgoCn+6JWF430B3lQQAAAAAAHoVAsTeQi4Tbx0XveWBKcumDPBp6oHljLzqu9Z9//S2tPLauu4vD01Ky616/rMMl8F7rhl8XWKkW+oBAAAAAAC9EAFi7+Kv9rn/uvh/3zdpRkJE46uSJHydWjDv9f3r9pyzNGq9gm5WWGX6v03HXHrgzBgRccdVce4qCQAAAAAA9EIEiL1RrE7rPBhxYPMHI85fy8GI7mSssz228VilwdJwcFhUwJ9vHUHXZQAAAAAA0J0IEHuvK+J0H9478YmbE4KaOhixuJqDEd3GIUl//u+J88U1DQcjgtSvLB2r8qHtMgAAAAAA6FYEiL2a82DErc6DEeVNH4x497oUDkbsZq/vOHPgTGnDEY1K8fKSpBC/JqJeAAAAAACALkWAiJ8PRry/6YMRHZLEwYjd6fOj+R9/l9NwRCaKT9+WOKip580BAAAAAAC6GgEifvLLwYjhfo2vcjBi9ziWU/nSFyddBh+4YchVQ8LdUg8AAAAAAAABIi5xRZzuwxWTWj4Y8f5//ZBZVNP4Kjoov9K0cvNxq/2SbZ43JfVdPLGfu0oCAAAAAAAgQISryx6MeDS74s53DnMwYufSm6yPbPixynhJ2+XR/YL/cPNwd5UEAAAAAAAgECCiOc6DET+6b9KU+LDGVzkYsXPZ7NKTW1IvlhsaDvYNVq9aNLrJDBcAAAAAAKDbkE2gJf1CtS8vHdPywYgL1u7/6nhB99fWk7z69akfssobjmhVitVLxgRpaLsMAAAAAADcjAARl9fywYhF1eZnPkm7758cjNhOmw7nbPsht+GIXCauWjQ6rqnQFgAAAAAAoJsRIKJVnAcjbmnFwYgVtZbGV9Gc786VvfG/My6Dj84adkWczi31AAAAAAAAuCBARBsE/Hww4uTmD0a87fV9HIzYShdKa/+0NdXhkBoOLryy39wrYtxVEgAAAAAAgAuFuwuA9+kXqn1l6Zgfsspf+/p0Vkmty1XnwYg7ThSuuHbwjIQIl6tmq/2HrPJzRfrc4orw4LoBfQInDNL1zpP+qoyWxzYeqzXbGg5eOSj0wRuGuKskAAAAAACAxggQ0U5XxOk+vHfSF8fy3/nmbJXR9bHlvArjk1tStw3IffjGoYMj/AVBqDZa/7Xv/LYjeXVW+8+zKgVBkMnE6cP7rJgxODpE06034FZWu2Plx6n5FcaGg/3DtM/OHyWXie6qCgAAAAAAoDECRLSfQi7eOi76moQ+65LP/zflov3SR3EFQfjxQsUd7xy+YWTkzJFRz36aXqo3C4IgCJIg/JKRORzSN+lFB8+UPnXriBkjXHcs9kiSJDz/acax7IqGg4Ean5eXjPH35a8kAAAAAADwLKQV6KgAtc/vZw697YqY13ecOXS21OWq82DEr1MLGow1scPObLX/6T+pkiBc2wsyxPX7sy79gAgKufjCwtG9ag8mAAAAAADwFjRRQefoF6p99ddj3rhjXFy4X/teQZKEv21Pzy03Xn6qN0s+Vfzet+dcBv9v9vAx/UPcUg8AAAAAAEDLCBDRmZwHIz5xc0L7+qKYrfa3d2d2elWeI7NQ//S2NId0ybPed1wV96ux0e4qCQAAAAAAoGUEiOhkzoMRtzw4ZcGV/drRDyT5VElFrWtLlp6hrKbusY3HTBZ7w8Fpw/rcM2OQu0oCAAAAAAC4LAJEdAnnwYjPLxjV1oUOSTp8rqwrSnIvs9X+2MajJT+1kflJfGTAX+YmykTaLgMAAAAAAM9FgIguJGv7DkRBEPIqetoxiJIkPPdp+ukCfcPBUH/V6sVJaqXcXVUBAAAAAAC0BgEiupDeZGvXKmunV+Je7357dldaUcMRlY/8pcVJfQJ93VUSAAAAAABAKyncXQB6smCtTztW7U4rigj0vSmpb7C2PZ1YPM036UXr92c1HBFF4clbEob3DXRXSQAAeLuysrKNGzeeP3++sLBQp9PFxcXNnz+/f//+LtOqq6s3bdqUkpJSVVWl0+kmTpy4aNEijUbTmnfRkbX1zGbz8uXLJ0+evGLFijYtBAAA8CgEiOhC/UP92rGq2mR5a1fme9+eu2pI2K/GRo8fqPPeUwLTcqv++knapV2XhbumD7o+MdJNFQEA4PWOHTu2atUqk8kkCEJgYGBBQUF+fv6hQ4eWL18+e/bs+mnl5eWPP/54WVmZIAi+vr7FxcXbt28/cuTISy+95Od3mW9RWrk2NTV18+bNWVlZoaGhU6ZMmTdvno/PJb89/fzzz00m04IFCzrx9gEAALofjzCjC0UFqwf28W/fWqvd8e3J4oc3/HjLq/ve2pVZWGXq3Nq6QVGV6Q+bj1ltjoaDMxIifnP1QHeVBACAt7NarW+//bbJZJo0adJHH320YcOGTZs2zZ071263r1u3Ljs7u37me++9V1ZWFhcX99Zbb3388cevvfZaeHh4Xl7ehx9+eNn30pq1ycnJTz311Pnz5wcNGlRXV7dp06Znn31WavBrQ4PBsG3btlmzZul0uk79GAAAAHQ3AkR0rcUT+3XwFUr15g0HLty2Zv8D6498k1Fks0uXX+MBjHW2xzYeq6i1NBwcFhXw1JwRXrufEgAA9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width="864" /></p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/figure2.png'</span>,</a>
<a class="sourceLine" id="cb7-2" data-line-number="2">       <span class="dt">plot =</span> g, <span class="dt">dpi =</span> <span class="dv">300</span>, <span class="dt">width =</span> <span class="dv">9</span>, <span class="dt">height =</span> <span class="dv">6</span>)</a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="kw">ggsave</span>(<span class="st">'figures/figure2.pdf'</span>,</a>
<a class="sourceLine" id="cb7-4" data-line-number="4">       <span class="dt">plot =</span> g,  <span class="dt">width =</span> <span class="dv">9</span>, <span class="dt">height =</span> <span class="dv">6</span>)</a></code></pre></div>
</div>
</div>
<div id="substantive-representation-text-analysis" class="section level2">
<h2>Substantive Representation (Text Analysis)</h2>
<div id="create-corpus" class="section level3">
<h3>Create corpus</h3>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">ted &lt;-<span class="st"> </span><span class="kw">corpus</span>(df<span class="op">$</span>text)</a>
<a class="sourceLine" id="cb8-2" data-line-number="2">ted_dfm &lt;-<span class="st"> </span><span class="kw">dfm</span>(ted,</a>
<a class="sourceLine" id="cb8-3" data-line-number="3">            <span class="dt">stem =</span> <span class="ot">TRUE</span>, <span class="dt">tolower =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb8-4" data-line-number="4">            <span class="dt">remove_twitter =</span> <span class="ot">FALSE</span>, <span class="dt">remove_punct =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb8-5" data-line-number="5">            <span class="dt">remove_url =</span> <span class="ot">FALSE</span>, <span class="dt">remove_numbers =</span><span class="ot">TRUE</span>, <span class="dt">verbose =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb8-6" data-line-number="6">            <span class="dt">remove =</span> <span class="kw">stopwords</span>(<span class="st">'english'</span>))</a></code></pre></div>
<pre><code>## Creating a dfm from a corpus input...</code></pre>
<pre><code>##    ... lowercasing</code></pre>
<pre><code>##    ... found 2,333 documents, 67,985 features</code></pre>
<pre><code>##    ... removed 175 features
##    ... stemming features (English)
## , trimmed 23401 feature variants
##    ... created a 2,333 x 44,409 sparse dfm
##    ... complete. 
## Elapsed time: 4.98 seconds.</code></pre>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="co"># feature trimming</span></a>
<a class="sourceLine" id="cb13-2" data-line-number="2">ted_dfm &lt;-<span class="st"> </span><span class="kw">dfm_trim</span>(ted_dfm, <span class="dt">max_docfreq =</span> <span class="fl">0.50</span>,<span class="dt">min_docfreq =</span> <span class="fl">0.01</span>,</a>
<a class="sourceLine" id="cb13-3" data-line-number="3">                    <span class="dt">docfreq_type =</span> <span class="st">'prop'</span>)</a>
<a class="sourceLine" id="cb13-4" data-line-number="4"><span class="kw">dim</span>(ted_dfm)</a></code></pre></div>
<pre><code>## [1] 2333 4805</code></pre>
</div>
<div id="run-structural-topic-models" class="section level3">
<h3>Run structural topic models</h3>
<p>Please note that the following block may take a considerable amount of time and, depending on your hardware, not all objects might fit in memory. In the replication files, you can find the file <code>ted_ready_for_analysis.RData</code>, which contains all the objects you need to run the remainder of our analysis.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">out &lt;-</a>
<a class="sourceLine" id="cb15-2" data-line-number="2"><span class="st">  </span><span class="kw">convert</span>(ted_dfm, <span class="dt">to =</span> <span class="st">'stm'</span>, <span class="dt">docvars =</span> df) <span class="co"># convert to dfm format</span></a>
<a class="sourceLine" id="cb15-3" data-line-number="3"></a>
<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="co"># running model with 20 topics</span></a>
<a class="sourceLine" id="cb15-5" data-line-number="5">stm_<span class="dv">20</span> &lt;-<span class="st"> </span><span class="kw">stm</span>(</a>
<a class="sourceLine" id="cb15-6" data-line-number="6">  out<span class="op">$</span>documents,</a>
<a class="sourceLine" id="cb15-7" data-line-number="7">  out<span class="op">$</span>vocab,</a>
<a class="sourceLine" id="cb15-8" data-line-number="8">  <span class="dt">data =</span> out<span class="op">$</span>meta,</a>
<a class="sourceLine" id="cb15-9" data-line-number="9">  <span class="dt">prevalence =</span>  <span class="op">~</span><span class="st"> </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">*</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-10" data-line-number="10">  <span class="dt">init.type =</span> <span class="st">'Spectral'</span>,</a>
<a class="sourceLine" id="cb15-11" data-line-number="11">  <span class="dt">K =</span> <span class="dv">20</span>,</a>
<a class="sourceLine" id="cb15-12" data-line-number="12">  <span class="dt">verbose =</span> F</a>
<a class="sourceLine" id="cb15-13" data-line-number="13">)</a>
<a class="sourceLine" id="cb15-14" data-line-number="14"></a>
<a class="sourceLine" id="cb15-15" data-line-number="15">effect_stm_<span class="dv">20</span> &lt;-</a>
<a class="sourceLine" id="cb15-16" data-line-number="16"><span class="st">  </span><span class="kw">estimateEffect</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">20</span> <span class="op">~</span><span class="st"> </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">*</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-17" data-line-number="17">                 stm_<span class="dv">20</span>,</a>
<a class="sourceLine" id="cb15-18" data-line-number="18">                 <span class="dt">metadata =</span> out<span class="op">$</span>meta)</a>
<a class="sourceLine" id="cb15-19" data-line-number="19"></a>
<a class="sourceLine" id="cb15-20" data-line-number="20"><span class="co"># running model with 30 topics</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21">stm_<span class="dv">30</span> &lt;-<span class="st"> </span><span class="kw">stm</span>(</a>
<a class="sourceLine" id="cb15-22" data-line-number="22">  out<span class="op">$</span>documents,</a>
<a class="sourceLine" id="cb15-23" data-line-number="23">  out<span class="op">$</span>vocab,</a>
<a class="sourceLine" id="cb15-24" data-line-number="24">  <span class="dt">data =</span> out<span class="op">$</span>meta,</a>
<a class="sourceLine" id="cb15-25" data-line-number="25">  <span class="dt">prevalence =</span>  <span class="op">~</span><span class="st">  </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">+</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-26" data-line-number="26">  <span class="dt">init.type =</span> <span class="st">'Spectral'</span>,</a>
<a class="sourceLine" id="cb15-27" data-line-number="27">  <span class="dt">K =</span> <span class="dv">30</span>,</a>
<a class="sourceLine" id="cb15-28" data-line-number="28">  <span class="dt">verbose =</span> F</a>
<a class="sourceLine" id="cb15-29" data-line-number="29">)</a>
<a class="sourceLine" id="cb15-30" data-line-number="30"></a>
<a class="sourceLine" id="cb15-31" data-line-number="31">effect_stm_<span class="dv">30</span> &lt;-</a>
<a class="sourceLine" id="cb15-32" data-line-number="32"><span class="st">  </span><span class="kw">estimateEffect</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">30</span> <span class="op">~</span><span class="st"> </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">+</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-33" data-line-number="33">                 stm_<span class="dv">30</span>,</a>
<a class="sourceLine" id="cb15-34" data-line-number="34">                 <span class="dt">metadata =</span> out<span class="op">$</span>meta)</a>
<a class="sourceLine" id="cb15-35" data-line-number="35"></a>
<a class="sourceLine" id="cb15-36" data-line-number="36"><span class="co"># running model with 50 topics</span></a>
<a class="sourceLine" id="cb15-37" data-line-number="37">stm_<span class="dv">50</span> &lt;-<span class="st"> </span><span class="kw">stm</span>(</a>
<a class="sourceLine" id="cb15-38" data-line-number="38">  out<span class="op">$</span>documents,</a>
<a class="sourceLine" id="cb15-39" data-line-number="39">  out<span class="op">$</span>vocab,</a>
<a class="sourceLine" id="cb15-40" data-line-number="40">  <span class="dt">data =</span> out<span class="op">$</span>meta,</a>
<a class="sourceLine" id="cb15-41" data-line-number="41">  <span class="dt">prevalence =</span>  <span class="op">~</span><span class="st">  </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">+</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-42" data-line-number="42">  <span class="dt">init.type =</span> <span class="st">'Spectral'</span>,</a>
<a class="sourceLine" id="cb15-43" data-line-number="43">  <span class="dt">K =</span> <span class="dv">50</span>,</a>
<a class="sourceLine" id="cb15-44" data-line-number="44">  <span class="dt">verbose =</span> F</a>
<a class="sourceLine" id="cb15-45" data-line-number="45">)</a>
<a class="sourceLine" id="cb15-46" data-line-number="46"></a>
<a class="sourceLine" id="cb15-47" data-line-number="47">effect_stm_<span class="dv">50</span> &lt;-</a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="st">  </span><span class="kw">estimateEffect</span>(</a>
<a class="sourceLine" id="cb15-49" data-line-number="49">    <span class="dv">1</span><span class="op">:</span><span class="dv">50</span> <span class="op">~</span><span class="st"> </span><span class="kw">s</span>(date_num, <span class="dt">degree =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span>dummy_female <span class="op">+</span><span class="st"> </span>dummy_white,</a>
<a class="sourceLine" id="cb15-50" data-line-number="50">    stm_<span class="dv">50</span>,</a>
<a class="sourceLine" id="cb15-51" data-line-number="51">    <span class="dt">metadata =</span> out<span class="op">$</span>meta,</a>
<a class="sourceLine" id="cb15-52" data-line-number="52">    <span class="dt">uncertainty =</span>  <span class="st">'None'</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53">  )</a>
<a class="sourceLine" id="cb15-54" data-line-number="54"></a>
<a class="sourceLine" id="cb15-55" data-line-number="55"><span class="co"># save objects for stminsights</span></a>
<a class="sourceLine" id="cb15-56" data-line-number="56"><span class="kw">save.image</span>(<span class="st">'ted_topic_models.RData'</span>)</a>
<a class="sourceLine" id="cb15-57" data-line-number="57"></a>
<a class="sourceLine" id="cb15-58" data-line-number="58"></a>
<a class="sourceLine" id="cb15-59" data-line-number="59"><span class="co"># getting semantic coherence and exclusivity scores</span></a>
<a class="sourceLine" id="cb15-60" data-line-number="60">quality &lt;-</a>
<a class="sourceLine" id="cb15-61" data-line-number="61"><span class="st">  </span><span class="kw">get_diag</span>(</a>
<a class="sourceLine" id="cb15-62" data-line-number="62">    <span class="dt">models =</span> <span class="kw">list</span>(</a>
<a class="sourceLine" id="cb15-63" data-line-number="63">      <span class="dt">model_20 =</span> stm_<span class="dv">20</span>,</a>
<a class="sourceLine" id="cb15-64" data-line-number="64">      <span class="dt">model_30 =</span> stm_<span class="dv">30</span>,</a>
<a class="sourceLine" id="cb15-65" data-line-number="65">      <span class="dt">model_50 =</span> stm_<span class="dv">50</span></a>
<a class="sourceLine" id="cb15-66" data-line-number="66">    ),</a>
<a class="sourceLine" id="cb15-67" data-line-number="67">    <span class="dt">outobj =</span> out</a>
<a class="sourceLine" id="cb15-68" data-line-number="68">  )</a>
<a class="sourceLine" id="cb15-69" data-line-number="69"><span class="co"># plotting scores (figure S2)</span></a>
<a class="sourceLine" id="cb15-70" data-line-number="70">quality <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb15-71" data-line-number="71"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">nr_topics =</span> <span class="kw">as.factor</span>(nr_topics)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb15-72" data-line-number="72"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb15-73" data-line-number="73">    <span class="dt">x =</span> coherence,</a>
<a class="sourceLine" id="cb15-74" data-line-number="74">    <span class="dt">y =</span> exclusivity,</a>
<a class="sourceLine" id="cb15-75" data-line-number="75">    <span class="dt">color =</span> statistic,</a>
<a class="sourceLine" id="cb15-76" data-line-number="76">    <span class="dt">shape =</span> nr_topics</a>
<a class="sourceLine" id="cb15-77" data-line-number="77">  ))  <span class="op">+</span></a>
<a class="sourceLine" id="cb15-78" data-line-number="78"><span class="st">  </span><span class="kw">geom_text_repel</span>(<span class="kw">aes</span>(<span class="dt">label =</span> nr_topics),</a>
<a class="sourceLine" id="cb15-79" data-line-number="79">                  <span class="dt">size =</span> <span class="fl">4.5</span>,</a>
<a class="sourceLine" id="cb15-80" data-line-number="80">                  <span class="dt">show.legend =</span> <span class="ot">FALSE</span>) <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb15-81" data-line-number="81"><span class="st">  </span><span class="kw">labs</span>(</a>
<a class="sourceLine" id="cb15-82" data-line-number="82">    <span class="dt">x =</span> <span class="st">'Semantic Coherence'</span>,</a>
<a class="sourceLine" id="cb15-83" data-line-number="83">    <span class="dt">y =</span> <span class="st">'Exclusivity'</span>,</a>
<a class="sourceLine" id="cb15-84" data-line-number="84">    <span class="dt">shape =</span> <span class="st">'No. of topics'</span>,</a>
<a class="sourceLine" id="cb15-85" data-line-number="85">    <span class="dt">color =</span> <span class="st">'Statistic'</span></a>
<a class="sourceLine" id="cb15-86" data-line-number="86">  ) <span class="op">+</span><span class="st"> </span><span class="kw">theme_light</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb15-87" data-line-number="87"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb15-88" data-line-number="88">    <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.9</span>, <span class="fl">0.7</span>),</a>
<a class="sourceLine" id="cb15-89" data-line-number="89">    <span class="dt">legend.background =</span> <span class="kw">element_rect</span>(<span class="dt">color =</span> <span class="st">&quot;grey50&quot;</span>)</a>
<a class="sourceLine" id="cb15-90" data-line-number="90">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb15-91" data-line-number="91"><span class="st">  </span><span class="kw">scale_color_brewer</span>(<span class="dt">palette =</span> <span class="st">'Set1'</span>)</a>
<a class="sourceLine" id="cb15-92" data-line-number="92"></a>
<a class="sourceLine" id="cb15-93" data-line-number="93"><span class="kw">ggsave</span>(<span class="st">'figures/supporting_information_figure2.png'</span>,</a>
<a class="sourceLine" id="cb15-94" data-line-number="94">      <span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">6</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a>
<a class="sourceLine" id="cb15-95" data-line-number="95"></a>
<a class="sourceLine" id="cb15-96" data-line-number="96"></a>
<a class="sourceLine" id="cb15-97" data-line-number="97"><span class="co"># run stminsights to validate topic models</span></a>
<a class="sourceLine" id="cb15-98" data-line-number="98"><span class="kw">library</span>(stminsights)</a>
<a class="sourceLine" id="cb15-99" data-line-number="99"><span class="kw">run_stminsights</span>()</a></code></pre></div>
<p>After inspecting the different topic models, we found that the model with 30 topics includes the most useful topics for our analysis. We store objects for this model in a separate file.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1"><span class="kw">save</span>(out, ted_dfm, stm_<span class="dv">30</span>, effect_stm_<span class="dv">30</span>, <span class="dt">file =</span> <span class="st">&quot;ted_ready_for_analysis.RData&quot;</span>)</a>
<a class="sourceLine" id="cb16-2" data-line-number="2"><span class="kw">save.image</span>()</a>
<a class="sourceLine" id="cb16-3" data-line-number="3"><span class="kw">unlink</span>(<span class="st">&quot;ted_ready_for_analysis.RData&quot;</span>)</a></code></pre></div>
</div>
</div>
<div id="ted-talks---sentiment" class="section level2">
<h2>Ted Talks - Sentiment</h2>
<p>This code is used to calculate sentiment scores for more than 1,000,000 YouTube comments and might therefore talk quite some time to execute. Instead of running this code, you can load ‘ted_yt-comments_sentiment.tsv’ (see below), which contains a dataset with sentiment scores ready for analysis.</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">yt &lt;-<span class="st"> </span><span class="kw">read_tsv</span>(<span class="st">'ted_yt-comments.tsv'</span>)</a>
<a class="sourceLine" id="cb17-2" data-line-number="2"></a>
<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="co"># prepare texts for sentiment analysis</span></a>
<a class="sourceLine" id="cb17-4" data-line-number="4">clean_text &lt;-<span class="st"> </span><span class="cf">function</span>(x) {</a>
<a class="sourceLine" id="cb17-5" data-line-number="5">  x &lt;-<span class="st"> </span>x <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">replace_internet_slang</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="st">    </span><span class="kw">replace_emoticon</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-7" data-line-number="7"><span class="st">    </span><span class="kw">replace_emoji_identifier</span>()</a>
<a class="sourceLine" id="cb17-8" data-line-number="8">  <span class="kw">return</span>(x)</a>
<a class="sourceLine" id="cb17-9" data-line-number="9">}</a>
<a class="sourceLine" id="cb17-10" data-line-number="10"></a>
<a class="sourceLine" id="cb17-11" data-line-number="11"><span class="co"># compute sentiment</span></a>
<a class="sourceLine" id="cb17-12" data-line-number="12">yt<span class="op">$</span>comment_processed &lt;-<span class="st"> </span><span class="kw">clean_text</span>(yt<span class="op">$</span>comment_text)</a>
<a class="sourceLine" id="cb17-13" data-line-number="13"></a>
<a class="sourceLine" id="cb17-14" data-line-number="14">sents &lt;-<span class="st"> </span>yt <span class="op">%&gt;%</span><span class="st">  </span><span class="kw">select</span>(yt_id, comment_id, comment_processed) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-15" data-line-number="15"><span class="st">  </span><span class="kw">get_sentences</span>() </a>
<a class="sourceLine" id="cb17-16" data-line-number="16"></a>
<a class="sourceLine" id="cb17-17" data-line-number="17">out &lt;-<span class="st"> </span><span class="kw">with</span>(sents, <span class="kw">sentiment_by</span>(comment_processed, comment_id))</a>
<a class="sourceLine" id="cb17-18" data-line-number="18"><span class="kw">colnames</span>(out) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'comment_id'</span>,</a>
<a class="sourceLine" id="cb17-19" data-line-number="19">   <span class="st">'comment_sents_word_count'</span>, <span class="st">'comment_avg_sentiment_sd'</span>, <span class="st">'comment_avg_sentiment'</span>)</a>
<a class="sourceLine" id="cb17-20" data-line-number="20"></a>
<a class="sourceLine" id="cb17-21" data-line-number="21"><span class="co"># merge datasets by comment id's</span></a>
<a class="sourceLine" id="cb17-22" data-line-number="22">yt &lt;-<span class="st"> </span>yt <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">left_join</span>(out, <span class="dt">by =</span> <span class="st">'comment_id'</span>)</a>
<a class="sourceLine" id="cb17-23" data-line-number="23"></a>
<a class="sourceLine" id="cb17-24" data-line-number="24"><span class="co"># find examples with high negative sentiment</span></a>
<a class="sourceLine" id="cb17-25" data-line-number="25">sent_neg &lt;-<span class="st"> </span>yt <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(comment_avg_sentiment) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-26" data-line-number="26"><span class="st">  </span><span class="kw">select</span>(comment_processed, comment_avg_sentiment, yt_id) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb17-27" data-line-number="27"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">polarity =</span> <span class="st">'neg'</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-28" data-line-number="28"><span class="st">  </span><span class="kw">head</span>(<span class="dv">50</span>) </a>
<a class="sourceLine" id="cb17-29" data-line-number="29"></a>
<a class="sourceLine" id="cb17-30" data-line-number="30"><span class="co"># find examples with high positive sentiment</span></a>
<a class="sourceLine" id="cb17-31" data-line-number="31">sent_pos &lt;-<span class="st"> </span>yt <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(comment_avg_sentiment)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-32" data-line-number="32"><span class="st">  </span><span class="kw">select</span>(comment_processed, comment_avg_sentiment, yt_id) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb17-33" data-line-number="33"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">polarity =</span> <span class="st">'pos'</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-34" data-line-number="34"><span class="st">  </span><span class="kw">head</span>(<span class="dv">50</span>) </a>
<a class="sourceLine" id="cb17-35" data-line-number="35"></a>
<a class="sourceLine" id="cb17-36" data-line-number="36"><span class="co"># compute averages</span></a>
<a class="sourceLine" id="cb17-37" data-line-number="37">yt_sent &lt;-<span class="st"> </span>yt <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">group_by</span>(yt_id) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">summarize</span>(</a>
<a class="sourceLine" id="cb17-38" data-line-number="38">    <span class="dt">yt_comments_avg_wordcount =</span> <span class="kw">mean</span>(comment_sents_word_count),</a>
<a class="sourceLine" id="cb17-39" data-line-number="39">    <span class="dt">yt_comments_total_wordcount =</span> <span class="kw">sum</span>(comment_sents_word_count),</a>
<a class="sourceLine" id="cb17-40" data-line-number="40">    <span class="dt">yt_comments_avg_sentiment =</span> <span class="kw">mean</span>(comment_avg_sentiment))</a>
<a class="sourceLine" id="cb17-41" data-line-number="41"></a>
<a class="sourceLine" id="cb17-42" data-line-number="42"><span class="co">#save sentiment scores </span></a>
<a class="sourceLine" id="cb17-43" data-line-number="43"><span class="kw">write_tsv</span>(yt_sent, <span class="st">'ted_yt-comments_sentiment.tsv'</span>)</a></code></pre></div>
</div>
<div id="reload-data-for-analysis" class="section level2">
<h2>Reload data for analysis</h2>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1"><span class="co"># load main dataset</span></a>
<a class="sourceLine" id="cb18-2" data-line-number="2">df &lt;-<span class="st"> </span><span class="kw">read_tsv</span>(<span class="st">'ted_main_dataset.tsv'</span>)</a>
<a class="sourceLine" id="cb18-3" data-line-number="3">df &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span><span class="kw">is.na</span>(speaker_image_nr_faces)) </a>
<a class="sourceLine" id="cb18-4" data-line-number="4"></a>
<a class="sourceLine" id="cb18-5" data-line-number="5"><span class="co"># load topic models</span></a>
<a class="sourceLine" id="cb18-6" data-line-number="6"><span class="kw">load</span>(<span class="st">'ted_ready_for_analysis.RData'</span>)</a>
<a class="sourceLine" id="cb18-7" data-line-number="7"></a>
<a class="sourceLine" id="cb18-8" data-line-number="8"><span class="co"># create several variables for analysis</span></a>
<a class="sourceLine" id="cb18-9" data-line-number="9">df &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(date)</a>
<a class="sourceLine" id="cb18-10" data-line-number="10">df<span class="op">$</span>date_num &lt;-<span class="st"> </span><span class="kw">factor</span>(df<span class="op">$</span>date) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.numeric</span>()</a>
<a class="sourceLine" id="cb18-11" data-line-number="11">df<span class="op">$</span>year &lt;-<span class="kw">str_sub</span>(df<span class="op">$</span>date, <span class="dv">1</span>, <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb18-12" data-line-number="12">df<span class="op">$</span>yt_views_l &lt;-<span class="st"> </span><span class="kw">log</span>(df<span class="op">$</span>yt_views <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)</a>
<a class="sourceLine" id="cb18-13" data-line-number="13">df<span class="op">$</span>yt_dislikes_l &lt;-<span class="st"> </span><span class="kw">log</span>(df<span class="op">$</span>yt_dislikes <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)</a>
<a class="sourceLine" id="cb18-14" data-line-number="14">df<span class="op">$</span>yt_comments_l &lt;-<span class="st"> </span><span class="kw">log</span>(df<span class="op">$</span>yt_comments <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)</a>
<a class="sourceLine" id="cb18-15" data-line-number="15">df<span class="op">$</span>yt_date_num &lt;-<span class="st"> </span>df<span class="op">$</span>yt_date <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">str_sub</span>(<span class="dv">1</span>, <span class="dv">4</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.integer</span>()</a>
<a class="sourceLine" id="cb18-16" data-line-number="16">df<span class="op">$</span>factor_female &lt;-<span class="st"> </span><span class="kw">as.factor</span>(df<span class="op">$</span>dummy_female)</a>
<a class="sourceLine" id="cb18-17" data-line-number="17">df<span class="op">$</span>factor_white &lt;-<span class="st"> </span><span class="kw">as.factor</span>(df<span class="op">$</span>dummy_white)</a>
<a class="sourceLine" id="cb18-18" data-line-number="18"><span class="kw">levels</span>(df<span class="op">$</span>factor_female) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'male'</span>, <span class="st">'female'</span>)</a>
<a class="sourceLine" id="cb18-19" data-line-number="19"><span class="kw">levels</span>(df<span class="op">$</span>factor_white)[<span class="kw">levels</span>(df<span class="op">$</span>factor_white)<span class="op">==</span><span class="st"> </span><span class="dv">1</span>] &lt;-<span class="st"> 'white'</span></a>
<a class="sourceLine" id="cb18-20" data-line-number="20"><span class="kw">levels</span>(df<span class="op">$</span>factor_white)[<span class="kw">levels</span>(df<span class="op">$</span>factor_white)<span class="op">==</span><span class="st"> </span><span class="dv">0</span>] &lt;-<span class="st"> 'non-white'</span></a>
<a class="sourceLine" id="cb18-21" data-line-number="21">df<span class="op">$</span>factor_white &lt;-<span class="st"> </span><span class="kw">factor</span>(df<span class="op">$</span>factor_white, <span class="dt">levels =</span> <span class="kw">c</span>(<span class="st">'white'</span>, <span class="st">'non-white'</span>))</a>
<a class="sourceLine" id="cb18-22" data-line-number="22"></a>
<a class="sourceLine" id="cb18-23" data-line-number="23"><span class="co"># read sentiment scores</span></a>
<a class="sourceLine" id="cb18-24" data-line-number="24">yt_sent &lt;-<span class="st"> </span><span class="kw">read_tsv</span>(<span class="st">'~/Dropbox/kasus/PhD/projects/ted/data/yt_comments_sentiment.tsv'</span>)</a>
<a class="sourceLine" id="cb18-25" data-line-number="25">df &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">left_join</span>(yt_sent, <span class="dt">by =</span> <span class="st">'yt_id'</span>)</a>
<a class="sourceLine" id="cb18-26" data-line-number="26"><span class="co"># normalize sentiment scores</span></a>
<a class="sourceLine" id="cb18-27" data-line-number="27">df<span class="op">$</span>sentiment &lt;-<span class="st"> </span>df<span class="op">$</span>yt_comments_avg_sentiment <span class="op">-</span><span class="st">  </span></a>
<a class="sourceLine" id="cb18-28" data-line-number="28"><span class="st">  </span><span class="kw">mean</span>(df<span class="op">$</span>yt_comments_avg_sentiment, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb18-29" data-line-number="29"></a>
<a class="sourceLine" id="cb18-30" data-line-number="30"><span class="co"># extract stm topic proportions</span></a>
<a class="sourceLine" id="cb18-31" data-line-number="31">props &lt;-<span class="st"> </span><span class="kw">as.data.frame</span>(stm_<span class="dv">30</span><span class="op">$</span>theta) <span class="co"># topic proportions</span></a>
<a class="sourceLine" id="cb18-32" data-line-number="32"><span class="kw">colnames</span>(props) &lt;-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">'T_'</span>, <span class="kw">as.character</span>(<span class="dv">1</span><span class="op">:</span><span class="kw">ncol</span>(props))) <span class="co"># topic names</span></a>
<a class="sourceLine" id="cb18-33" data-line-number="33">props<span class="op">$</span>maxprop &lt;-<span class="st">  </span><span class="kw">apply</span>(props, <span class="dv">1</span>, which.max)</a>
<a class="sourceLine" id="cb18-34" data-line-number="34">df &lt;-<span class="st"> </span><span class="kw">bind_cols</span>(df, props)</a>
<a class="sourceLine" id="cb18-35" data-line-number="35"><span class="co"># create identifier for inequality topic</span></a>
<a class="sourceLine" id="cb18-36" data-line-number="36">df<span class="op">$</span>max_t4 &lt;-<span class="st"> </span><span class="kw">ifelse</span>(df<span class="op">$</span>maxprop <span class="op">==</span><span class="st"> </span><span class="dv">4</span>, <span class="dv">1</span>, <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb18-37" data-line-number="37">df<span class="op">$</span>maxprop_f &lt;-<span class="st"> </span><span class="kw">to_factor</span>(df<span class="op">$</span>maxprop, <span class="dt">ref.lvl =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb18-38" data-line-number="38"></a>
<a class="sourceLine" id="cb18-39" data-line-number="39"><span class="co"># vector of topic labels</span></a>
<a class="sourceLine" id="cb18-40" data-line-number="40">topic_labels &lt;-<span class="st"> </span></a>
<a class="sourceLine" id="cb18-41" data-line-number="41"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb18-42" data-line-number="42">    <span class="st">'work &amp; misc.'</span>,</a>
<a class="sourceLine" id="cb18-43" data-line-number="43">    <span class="st">'games &amp; music'</span>,</a>
<a class="sourceLine" id="cb18-44" data-line-number="44">    <span class="st">'stopwords'</span>,</a>
<a class="sourceLine" id="cb18-45" data-line-number="45">    <span class="st">'inequality'</span>,</a>
<a class="sourceLine" id="cb18-46" data-line-number="46">    <span class="st">'countries &amp; poverty'</span>,</a>
<a class="sourceLine" id="cb18-47" data-line-number="47">    <span class="st">'universe &amp; physics'</span>,</a>
<a class="sourceLine" id="cb18-48" data-line-number="48">    <span class="st">'family'</span>,</a>
<a class="sourceLine" id="cb18-49" data-line-number="49">    <span class="st">'war &amp; terror'</span>,</a>
<a class="sourceLine" id="cb18-50" data-line-number="50">    <span class="st">'astronautics'</span>,</a>
<a class="sourceLine" id="cb18-51" data-line-number="51">    <span class="st">'genetics'</span>,</a>
<a class="sourceLine" id="cb18-52" data-line-number="52">    <span class="st">'architecture &amp; design'</span>,</a>
<a class="sourceLine" id="cb18-53" data-line-number="53">    <span class="st">'environment'</span>,</a>
<a class="sourceLine" id="cb18-54" data-line-number="54">    <span class="st">'materials &amp; sustainability'</span>,</a>
<a class="sourceLine" id="cb18-55" data-line-number="55">    <span class="st">'law &amp; politics'</span>,</a>
<a class="sourceLine" id="cb18-56" data-line-number="56">    <span class="st">'art'</span>,</a>
<a class="sourceLine" id="cb18-57" data-line-number="57">    <span class="st">'food &amp; farming'</span>,</a>
<a class="sourceLine" id="cb18-58" data-line-number="58">    <span class="st">'language &amp; writing'</span>,</a>
<a class="sourceLine" id="cb18-59" data-line-number="59">    <span class="st">'religion &amp; mystery'</span>,</a>
<a class="sourceLine" id="cb18-60" data-line-number="60">    <span class="st">'money &amp; business'</span>,</a>
<a class="sourceLine" id="cb18-61" data-line-number="61">    <span class="st">'neuroscience &amp; robotics'</span>,</a>
<a class="sourceLine" id="cb18-62" data-line-number="62">    <span class="st">'children &amp; school'</span>,</a>
<a class="sourceLine" id="cb18-63" data-line-number="63">    <span class="st">'computers &amp; technology'</span>,</a>
<a class="sourceLine" id="cb18-64" data-line-number="64">     <span class="st">'general science'</span>,</a>
<a class="sourceLine" id="cb18-65" data-line-number="65">    <span class="st">'emotions &amp; psychology'</span>,</a>
<a class="sourceLine" id="cb18-66" data-line-number="66">    <span class="st">'data &amp; internet'</span>,</a>
<a class="sourceLine" id="cb18-67" data-line-number="67">    <span class="st">'transportation'</span>,</a>
<a class="sourceLine" id="cb18-68" data-line-number="68">    <span class="st">'animals &amp; nature'</span>,</a>
<a class="sourceLine" id="cb18-69" data-line-number="69">    <span class="st">'body &amp; sports'</span>,</a>
<a class="sourceLine" id="cb18-70" data-line-number="70">    <span class="st">'medical'</span>,</a>
<a class="sourceLine" id="cb18-71" data-line-number="71">    <span class="st">'ocean &amp; sea'</span> )</a></code></pre></div>
</div>
<div id="representation-descriptives-for-2016-2017-table-1" class="section level2">
<h2>Representation descriptives for 2016 &amp; 2017 (Table 1)</h2>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1">df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(year <span class="op">%in%</span><span class="st">  </span><span class="kw">c</span>(<span class="st">'2016'</span>, <span class="st">'2017'</span>) <span class="op">&amp;</span><span class="st"> </span>speaker_gender <span class="op">!=</span><span class="st"> 'undefined'</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-2" data-line-number="2"><span class="st">  </span><span class="kw">count</span>(speaker_gender) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb19-3" data-line-number="3"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">rel =</span> <span class="kw">round</span>(n <span class="op">/</span><span class="st"> </span><span class="kw">sum</span>(n), <span class="dv">3</span>))</a></code></pre></div>
<pre><code>## # A tibble: 2 x 3
##   speaker_gender     n   rel
##   &lt;chr&gt;          &lt;int&gt; &lt;dbl&gt;
## 1 female           163 0.472
## 2 male             182 0.528</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(year <span class="op">%in%</span><span class="st">  </span><span class="kw">c</span>(<span class="st">'2016'</span>, <span class="st">'2017'</span>) <span class="op">&amp;</span><span class="st"> </span>speaker_gender <span class="op">!=</span><span class="st"> 'undefined'</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-2" data-line-number="2"><span class="st">  </span><span class="kw">count</span>(speaker_ethnicity) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-3" data-line-number="3"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">rel =</span> <span class="kw">round</span>(n <span class="op">/</span><span class="st"> </span><span class="kw">sum</span>(n), <span class="dv">3</span>))</a></code></pre></div>
<pre><code>## # A tibble: 5 x 3
##   speaker_ethnicity     n   rel
##   &lt;chr&gt;             &lt;int&gt; &lt;dbl&gt;
## 1 Asian                15 0.043
## 2 Black                39 0.113
## 3 Hispanic             22 0.064
## 4 Other                12 0.035
## 5 White               257 0.745</code></pre>
</div>
<div id="density-of-ethnicity-annotations-figure-s1" class="section level2">
<h2>Density of ethnicity annotations (Figure S1)</h2>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" data-line-number="1">eth &lt;-<span class="st"> </span>df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_face_asian,</a>
<a class="sourceLine" id="cb23-2" data-line-number="2">                     speaker_face_black,</a>
<a class="sourceLine" id="cb23-3" data-line-number="3">                     speaker_face_white,</a>
<a class="sourceLine" id="cb23-4" data-line-number="4">                     speaker_face_hispanic,</a>
<a class="sourceLine" id="cb23-5" data-line-number="5">                     speaker_face_other) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb23-6" data-line-number="6"><span class="st">  </span><span class="kw">set_names</span>(<span class="op">~</span><span class="st"> </span><span class="kw">str_replace_all</span>(., <span class="st">'speaker_face_'</span>, <span class="st">''</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb23-7" data-line-number="7"><span class="st">  </span><span class="kw">gather</span>(<span class="dt">key =</span> ethnicity, <span class="dt">value =</span> prob) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb23-8" data-line-number="8"><span class="st">  </span><span class="kw">drop_na</span>()</a>
<a class="sourceLine" id="cb23-9" data-line-number="9">  </a>
<a class="sourceLine" id="cb23-10" data-line-number="10">eth <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> prob, <span class="dt">y =</span> ethnicity, <span class="dt">fill =</span> ..x..)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb23-11" data-line-number="11"><span class="st">  </span><span class="kw">geom_density_ridges_gradient</span>() <span class="op">+</span><span class="st">  </span></a>
<a class="sourceLine" id="cb23-12" data-line-number="12"><span class="st">  </span>viridis<span class="op">::</span><span class="kw">scale_fill_viridis</span>(<span class="dt">option =</span> <span class="st">'C'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb23-13" data-line-number="13"><span class="st">     </span><span class="kw">theme_light</span>(<span class="dt">base_size =</span> <span class="dv">13</span>) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb23-14" data-line-number="14"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">vjust =</span> <span class="dv">0</span>), <span class="dt">legend.position=</span><span class="st">&quot;none&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb23-15" data-line-number="15"><span class="st">   </span><span class="kw">scale_x_continuous</span>(<span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.01</span>, <span class="dv">0</span>), <span class="dt">labels =</span> scales<span class="op">::</span>percent) <span class="op">+</span></a>
<a class="sourceLine" id="cb23-16" data-line-number="16"><span class="st">   </span><span class="kw">scale_y_discrete</span>(<span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.01</span>, <span class="dv">0</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb23-17" data-line-number="17"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Probability'</span>, <span class="dt">y =</span> <span class="st">'Ethnicity'</span>)</a></code></pre></div>
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width="768" /></p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/supporting_information_figure1.png'</span>,</a>
<a class="sourceLine" id="cb24-2" data-line-number="2"><span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">6</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a></code></pre></div>
<div id="topic-prevalence-effects-figure-s3" class="section level3">
<h3>Topic prevalence effects (Figure S3)</h3>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1">pal &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'#377eb8'</span>, <span class="st">'#e41a1c'</span>, <span class="st">'#4daf4a'</span>)</a>
<a class="sourceLine" id="cb25-2" data-line-number="2"></a>
<a class="sourceLine" id="cb25-3" data-line-number="3">get_date &lt;-<span class="st"> </span><span class="cf">function</span>(x, <span class="dt">startdate =</span> <span class="st">&quot;2006-06-01&quot;</span>) {</a>
<a class="sourceLine" id="cb25-4" data-line-number="4">  startdate &lt;-<span class="st"> </span><span class="kw">ymd</span>(startdate)</a>
<a class="sourceLine" id="cb25-5" data-line-number="5">  x &lt;-<span class="st"> </span><span class="kw">round</span>(x, <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb25-6" data-line-number="6">  <span class="kw">return</span>(startdate <span class="op">+</span><span class="st"> </span><span class="kw">months</span>(x <span class="op">-</span><span class="st"> </span><span class="dv">1</span>))</a>
<a class="sourceLine" id="cb25-7" data-line-number="7">}</a>
<a class="sourceLine" id="cb25-8" data-line-number="8"></a>
<a class="sourceLine" id="cb25-9" data-line-number="9"></a>
<a class="sourceLine" id="cb25-10" data-line-number="10">effects_white &lt;-<span class="st"> </span><span class="kw">get_effects</span>(<span class="dt">estimates =</span> effect_stm_<span class="dv">30</span>,</a>
<a class="sourceLine" id="cb25-11" data-line-number="11">                             <span class="dt">variable =</span> <span class="st">'dummy_white'</span>,</a>
<a class="sourceLine" id="cb25-12" data-line-number="12">                             <span class="dt">type =</span> <span class="st">'pointestimate'</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-13" data-line-number="13"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">value =</span> <span class="kw">if_else</span>(value <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">'Other'</span>, <span class="st">'White'</span>)  <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.factor</span>())</a>
<a class="sourceLine" id="cb25-14" data-line-number="14"></a>
<a class="sourceLine" id="cb25-15" data-line-number="15"></a>
<a class="sourceLine" id="cb25-16" data-line-number="16">effects_female &lt;-<span class="st"> </span><span class="kw">get_effects</span>(<span class="dt">estimates =</span> effect_stm_<span class="dv">30</span>,</a>
<a class="sourceLine" id="cb25-17" data-line-number="17">                              <span class="dt">variable =</span> <span class="st">'dummy_female'</span>,</a>
<a class="sourceLine" id="cb25-18" data-line-number="18">                              <span class="dt">type =</span> <span class="st">'pointestimate'</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-19" data-line-number="19"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">value =</span> <span class="kw">if_else</span>(value <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">'Male'</span>, <span class="st">'Female'</span>)  <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.factor</span>())</a>
<a class="sourceLine" id="cb25-20" data-line-number="20"></a>
<a class="sourceLine" id="cb25-21" data-line-number="21">effects_time &lt;-<span class="st"> </span><span class="kw">get_effects</span>(<span class="dt">estimates =</span> effect_stm_<span class="dv">30</span>,</a>
<a class="sourceLine" id="cb25-22" data-line-number="22">                            <span class="dt">variable =</span> <span class="st">'date_num'</span>,</a>
<a class="sourceLine" id="cb25-23" data-line-number="23">                            <span class="dt">type =</span> <span class="st">'continuous'</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-24" data-line-number="24"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">date =</span> <span class="kw">get_date</span>(value))</a>
<a class="sourceLine" id="cb25-25" data-line-number="25"></a>
<a class="sourceLine" id="cb25-26" data-line-number="26"></a>
<a class="sourceLine" id="cb25-27" data-line-number="27"></a>
<a class="sourceLine" id="cb25-28" data-line-number="28">plot_female &lt;-<span class="st"> </span>effects_female <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(topic <span class="op">==</span><span class="st"> </span><span class="dv">4</span>)  <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-29" data-line-number="29"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> value, <span class="dt">x =</span> proportion, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-30" data-line-number="30"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-31" data-line-number="31"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb25-32" data-line-number="32">                                                    <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb25-33" data-line-number="33">                                                    <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb25-34" data-line-number="34"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb25-35" data-line-number="35">    <span class="kw">aes</span>(<span class="dt">xmin =</span> lower, <span class="dt">xmax =</span> upper),</a>
<a class="sourceLine" id="cb25-36" data-line-number="36">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb25-37" data-line-number="37">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb25-38" data-line-number="38">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb25-39" data-line-number="39">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb25-40" data-line-number="40"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb25-41" data-line-number="41">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb25-42" data-line-number="42">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb25-43" data-line-number="43">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb25-44" data-line-number="44">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb25-45" data-line-number="45"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb25-46" data-line-number="46">                     <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="op">-</span><span class="fl">0.01</span>, <span class="fl">0.00</span>, <span class="fl">0.01</span>, <span class="fl">0.02</span>, <span class="fl">0.03</span>, <span class="fl">0.04</span>, <span class="fl">0.05</span>, <span class="fl">0.06</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-47" data-line-number="47"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Topic proportion: inequality'</span>, <span class="dt">y =</span> <span class="st">'Gender'</span>) <span class="co">#+</span></a>
<a class="sourceLine" id="cb25-48" data-line-number="48"></a>
<a class="sourceLine" id="cb25-49" data-line-number="49"></a>
<a class="sourceLine" id="cb25-50" data-line-number="50">plot_white &lt;-<span class="st"> </span>effects_white <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(topic <span class="op">==</span><span class="st"> </span><span class="dv">4</span>)  <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-51" data-line-number="51"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> value, <span class="dt">x =</span> proportion, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-52" data-line-number="52"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-53" data-line-number="53"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb25-54" data-line-number="54">                                                    <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb25-55" data-line-number="55">                                                    <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb25-56" data-line-number="56"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb25-57" data-line-number="57">    <span class="kw">aes</span>(<span class="dt">xmin =</span> lower, <span class="dt">xmax =</span> upper),</a>
<a class="sourceLine" id="cb25-58" data-line-number="58">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb25-59" data-line-number="59">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb25-60" data-line-number="60">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb25-61" data-line-number="61">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb25-62" data-line-number="62"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb25-63" data-line-number="63">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb25-64" data-line-number="64">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb25-65" data-line-number="65">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb25-66" data-line-number="66">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb25-67" data-line-number="67"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb25-68" data-line-number="68">                     <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="op">-</span><span class="fl">0.01</span>, <span class="fl">0.00</span>, <span class="fl">0.01</span>, <span class="fl">0.02</span>, <span class="fl">0.03</span>, <span class="fl">0.04</span>, <span class="fl">0.05</span>, <span class="fl">0.06</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-69" data-line-number="69"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Topic proportion: inequality'</span>, <span class="dt">y =</span> <span class="st">'Ethnicity'</span>) </a>
<a class="sourceLine" id="cb25-70" data-line-number="70"></a>
<a class="sourceLine" id="cb25-71" data-line-number="71">break.vec &lt;-</a>
<a class="sourceLine" id="cb25-72" data-line-number="72"><span class="st">  </span><span class="kw">c</span>(<span class="kw">seq</span>(</a>
<a class="sourceLine" id="cb25-73" data-line-number="73">    <span class="dt">from =</span> <span class="kw">as.Date</span>(<span class="st">&quot;2006-06-01&quot;</span>),</a>
<a class="sourceLine" id="cb25-74" data-line-number="74">    <span class="dt">to =</span> <span class="kw">as.Date</span>(<span class="st">&quot;2017-05-01&quot;</span>),</a>
<a class="sourceLine" id="cb25-75" data-line-number="75">    <span class="dt">by =</span> <span class="st">&quot;6 months&quot;</span></a>
<a class="sourceLine" id="cb25-76" data-line-number="76">  ),</a>
<a class="sourceLine" id="cb25-77" data-line-number="77">  <span class="kw">as.Date</span>(<span class="st">&quot;2017-04-01&quot;</span>))</a>
<a class="sourceLine" id="cb25-78" data-line-number="78"></a>
<a class="sourceLine" id="cb25-79" data-line-number="79">plot_time &lt;-<span class="st"> </span>effects_time <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(topic <span class="op">==</span><span class="st"> </span><span class="dv">4</span>)  <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb25-80" data-line-number="80"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> date, <span class="dt">y =</span> proportion)) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-81" data-line-number="81"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-82" data-line-number="82"><span class="st">  </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">ymin =</span> lower, <span class="dt">ymax =</span> upper),</a>
<a class="sourceLine" id="cb25-83" data-line-number="83">              <span class="dt">alpha =</span> <span class="fl">0.2</span>,</a>
<a class="sourceLine" id="cb25-84" data-line-number="84">              <span class="dt">fill =</span> <span class="st">'#778899'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-85" data-line-number="85"><span class="st">  </span><span class="kw">scale_x_date</span>(</a>
<a class="sourceLine" id="cb25-86" data-line-number="86">    <span class="dt">breaks =</span> break.vec,</a>
<a class="sourceLine" id="cb25-87" data-line-number="87">    <span class="dt">minor_break =</span> break.vec,</a>
<a class="sourceLine" id="cb25-88" data-line-number="88">    <span class="dt">date_labels =</span> <span class="st">&quot;%Y/%m&quot;</span>,</a>
<a class="sourceLine" id="cb25-89" data-line-number="89">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.01</span>, <span class="fl">0.0</span>)</a>
<a class="sourceLine" id="cb25-90" data-line-number="90">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-91" data-line-number="91"><span class="st">  </span><span class="kw">scale_y_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">8</span>),</a>
<a class="sourceLine" id="cb25-92" data-line-number="92">                     <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.00</span>, <span class="dv">0</span>),</a>
<a class="sourceLine" id="cb25-93" data-line-number="93">                     <span class="dt">labels =</span> percent) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-94" data-line-number="94"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb25-95" data-line-number="95">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb25-96" data-line-number="96">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb25-97" data-line-number="97"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Date'</span> , <span class="dt">y  =</span> <span class="st">'Topic proportion: inequality'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-98" data-line-number="98"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-99" data-line-number="99"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb25-100" data-line-number="100"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb25-101" data-line-number="101">    <span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">90</span>, <span class="dt">hjust =</span> <span class="dv">1</span>),</a>
<a class="sourceLine" id="cb25-102" data-line-number="102">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb25-103" data-line-number="103">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb25-104" data-line-number="104">  )</a>
<a class="sourceLine" id="cb25-105" data-line-number="105"></a>
<a class="sourceLine" id="cb25-106" data-line-number="106"></a>
<a class="sourceLine" id="cb25-107" data-line-number="107"></a>
<a class="sourceLine" id="cb25-108" data-line-number="108">g1 &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(plot_white,</a>
<a class="sourceLine" id="cb25-109" data-line-number="109">                         plot_female,</a>
<a class="sourceLine" id="cb25-110" data-line-number="110">                         <span class="dt">ncol =</span> <span class="dv">2</span>,</a>
<a class="sourceLine" id="cb25-111" data-line-number="111">                         <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'A'</span>, <span class="st">'B'</span>))</a>
<a class="sourceLine" id="cb25-112" data-line-number="112"></a>
<a class="sourceLine" id="cb25-113" data-line-number="113">g2 &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(g1, plot_time, <span class="dt">ncol =</span> <span class="dv">1</span>,</a>
<a class="sourceLine" id="cb25-114" data-line-number="114">                         <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">''</span>, <span class="st">'C'</span>))</a>
<a class="sourceLine" id="cb25-115" data-line-number="115">g2</a></code></pre></div>
<p><img 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EAhhMFg+OFwdFk5E+4AoGGabBt4hULh6+u7cePGtLS0kydPLl261MnJSQhx8+bN9957r2vXruPHj//uu+90Ol1T9QgAACAvxj9AS5r8/DMd3F2FEDm6gsNnfpI7HQBoZZqsAGSkUqkmTJjw7bffZmZmRkREzJo1S2qPjo5+7bXXPD09FyxYcPjw4aqqqibvGmhdHubkZT7MkY7v3LtfVlYubz4AgEZj/AO0ABuVKnDGeJVKKYSIjb95K4lJdgDQAE1fAJKUl5efOXPm8OHDMTEx1dtLS0v37NkzY8aMwYMHnz9/vpl6ByxccWlZ8P6TG74OS0nPklpOXfj5o6+CL165zqIRANB6Mf4BmltHD+2E0cOl4x+OnC0sLpE3HwBoRWya9nYlJSXHjx/fu3fvwYMHq892bt++/ezZsydPnnzmzJndu3fn5ORcu3Zt4sSJp0+fHjVqVNPmAFi4krLybcEHjXN/jErLyvdHXSgoLJ78/DOyJAYAaBzGP0BLGj9yyK/Jqb+lZRYWl+w7FrN49mS5MwKA1qFpZgAVFBTs2bNn3rx57u7uAQEBu3fvlkY/7du3X7FixZkzZ9LT0zdv3jxr1qzPPvvs/v37n376qb29fVlZ2Z/+9KcmSQBoRaJi4mpXf4zOXPx3asaDlswHANA4jH8AWSgUisDp49RqWyHE9V/v/vvGHbkzAoDWwdwZQDt37ty7d+/x48fLysqMjZ6enrNnzw4MDBwzZoxKpapxiZ2d3bvvvnvjxo3vvvvu559/NjMBoHWpqKyM++VWPQEGIS5eudFlukeLpQQAaCjGP4C8XJ3bTR337P4T54UQ+0+c797Z07ldW7mTAgBLZ24BaMmSJcZjT0/POXPmBAYG+vn51R731NC1a1chhJeXl5kJAK3L/czs8orK+mPupma0TDIAgMZh/APIbuTQfreSUm4npZSWlf9w5OxrC6Yr5E4JACxcE6wB1KBxj9HYsWM///zznj17mp8A0IoUlpQ+OYblDAHA4jH+AeSlEGLOlOc3fRteXPQmbAoAACAASURBVFp25176xSvXRw3rL3dSAGDRzC0AnT17dvTo0aaPe4zGjh07duxYM3sHWp029nZNEgMAkBHjH8AStGurmT31+d37ooQQR6Mv9erWyd3VWe6kAMBymVsAio2NjY2Nfeedd5TK+taTTklJCQsLc3d3rz5l2kS3bt364YcfUlJS8vLyOnXq1K9fvwULFjg6OppyrU6nCwkJiY2NzcvL02q1zz333IIFCzQaTePC6ldaWrps2bLRo0cHBQU16EI8VTp4aFVKpb6qqp6YLh1YAAgALFoLjH8AmGKAT/fBfXsk3EyqqKgMPXh6xeJZqnr/VgLA08zcAtDq1auFEG+//Xb9A6Ds7OzVq1d36tSpoQOggwcPfvPNNwaDQalUOjo6JiUlJSUlnTt3bt26dZ06dar/2kePHq1evTo7O1sIYW9vn5WVFRkZGRcX98knn7Rt27ahYQkJCaGhocnJyW5ubn5+fnPnzrW1ta2RaklJybx58xr0gHjatLFTD+jdPeFmUj0xzwzq3WL5AAAaobnHPwBMN+vFMXfTs3T5hemZ2dE/xU8YNUzujADAQrVEgVyv1589e1YI8fDhwwZd+OjRox07dgghli5dGh4evmvXrm+++aZv3746nW7z5s0Gg6H+y7dt25adne3t7f3ll1/u2bNn48aNHh4eaWlpO3fubGhYdHT0+++/n5SU1LNnz7KyspCQkLVr11ZPoKioKCIiYtq0aVqttkHPiKfQtPEj27V97PwypUrZjm0sAKD1a/T4B0CDtLFTz536vLQC9KkLV9Iy+BsHAHVrTAHIrRqppX379m6P165du3feeUcI8cQ5OzUcPny4vLx81KhRs2bNkqbbeHh4vPvuuyqV6urVq9KcncfR6XSxsbFqtXrNmjVdunRRKBQ9evR47733hBDnzp0rLy83Pay8vHzr1q2enp5btmz58MMPt27dOnXq1Pj4+B9//NHYXWRkZGVl5dy5cxv0gHg6OTk6vLHQv2N7txrt0sClSl+1PfyorqCo5RMDANSjxcY/ABqqV7fOzw7tJ4SoqqoKO3ymovIJO64CwNOpMQWgR9VILTk5OY8er7i4WAp76623GtRRSkqKEGLUqFHVGz08PKSBVGpqaj3XxsTE6PX6QYMGtW/f3tjo7e3t7e1dXFwcFxdnetjdu3eLiooCAgJcXV2FECqVSprIfe3aNSk+Pz9///79/v7+Tk5ODXpAPLXcXJ3+sCRgyZwXPdxcpJYRA31W/MdMaeXC/IKinXuPl5dXyJojAOD/aLHxD4BGmD5+pJurkxDiwaO84+cuy50OAFiixqwB9Ne//tV4/Le//U0I8f7779f/Drytra2vr++kSZMa1JFOp9NoNNVLMxI7OzshRGlpfdtpX716VQgxdOjQGu3Dhg1LTk5OSEiQ6komhtWmUCj0er10HBERoVQqAwICTHwuQAihUCj69ux6MynlQXauEGL4wN5dOrX/3bypm3dGFhaXpGdl/+vAqSWzJ9f/NwsA0GJabPwDoBFsbW3mTRu35V8HqgyGC3HX+vbs2sOro9xJAYBlaUwB6P/9v/9nPJYGQH/5y19sbMxdT7q29evX124sKChIS0sTQnTt2rWea3Nzc4UQHTvW/He/Q4cOQoi8vDzTw7p166bRaPbt2+fr66vVavV6/Y4dOwwGw8CBA6U7HDp0aM6cOSZuTAbUw9XJ8dW5U7aGHKyoqLydlBIZdWH2i2PkTgoAIEQLjn8ANI5Xp/ZjRw45c/HfBoMh7NCZt5fObWNvJ3dSAGBBzB21LF++XAihUCiaIpn6VFRUFBQU/Pbbb3v27CkpKRkzZkz9b9TrdDohhIODQ412aWMv6ayJYWq1+s0339ywYUNQUFCvXr2ysrIePHgwbNgwPz8/IUR4eLharZ45c6aJD2IwGCof81qywWCoqnd3cFgf41LiVVVV0k+/Y3tt4LSxIQdOGwyG2Pibbi7t/EYMlDVHALB0Lf9fzxYb/0ju37+/cuXKvn37rl271pR4nU4XEhISGxubl5en1Wqfe+65BQsWaDQ19x8wMax+paWly5YtGz16dFBQUIMuBJrDRL/hv/6Wlpb5UFdQdOj0xcBp4+TOCAAsiLkFoC1btjRJHk+0atWq3377TTqeMmXKsmXL6o+XajfV93GX1FkAemLYuHHjnJ2dQ0ND79y5o9VqFy5cGBgYqFAoHj58eOzYsZdfftn00VJ+fn5MTEztdltbW51Od+/ePRPvA+tQWFgoHWRlZSmr/nttckc75bODev2UkCiEOBodW1lW2r2zh2wpAoDFe+LGoE2uxcY/QgiDwfD5558b9694okePHq1evVraK8Pe3j4rKysyMjIuLu6TTz6pPuAxMSwhISE0NDQ5OdnNzc3Pz2/u3LnSvhxGBw8eLCkpmTdvXhM8KmA2lVI5b/q4f+6IqKzU/3w1sU8Pr4G9veVOCgAsRauZt9y1a1eDwZCVlVVSUhITE+Pt7T1lypR64usfC1ZUVDQoTAgxZMiQIUOG1AgICwtzcHCYMWPGE7Kvpk2bNoMGDardfvv2bY1GY9xYBE8Je3t76cDJyan6T3+Cm1ul3hB37VeDwXA69toizxc6e/L/DQCom3XPnz127Nj169dNj9+2bVt2dra3t/e7777buXPn5OTkjz76KC0tbefOnStWrGhQWHR09IYNG+zt7aUZ0CEhITdv3vzb3/5mnPpUVFQUERExbdo0rVbbhI8MmMPDzWWy34gj0ZeEEJHHz3fr7Ono0LB5bQBgrRpcADIWQU6ePOnm5vb3v/+9QZf/+c9/bmiPEmkjVYPBcPbs2X/+85+bN292cXF59tlnHxfv7OxcUlJSVFRzL21pwoWLi0uDwuqUmZkZFRW1dOlS6Xf44uLiXbt2xcXFFRcX+/j4vPLKK927d699lVqt9vLyqt2emJioVqtZSOhpY/weVaPR1Pjpz542rris4savdysr9T8cjQlaPNPNhW3mAKAOLVAAkmv8k52d/f3335ser9PpYmNj1Wr1mjVrpG00evTo8d5777399tvnzp1btmyZWq02May8vHzr1q2enp7r1q1zdXXV6/Xbtm07evTojz/+OHr0aKm7yMjIysrKuXPnNu7pgGYyxnfQreTU5JT7RSWl+47FvDLnRbkzAgCL0OACUEJCgnQgLWTz/vvvN+jyRg+AJAqFYty4cSkpKT/88MOxY8fqLwBlZGTUruxILdULQKaE1SkkJMTZ2VmaiFRWVrZ69erU1NQuXbq4urrGx8fHx8d/9NFHffr0afhTAkIIoVQoFvq/sC30UOr9B0UlpdvDjq5YPMtBYy93XgDwNJJl/GMwGDZv3lxSUjJhwoRTp06ZcklMTIxerx86dGj1TVS9vb29vb2Tk5Pj4uKk7U1NCbt7925RUdErr7zi6uoqhFCpVEuWLDl69Oi1a9ekAlB+fv7+/fv9/f2dnPh+ApZFoVAEThu7afvesrLyG3fuxV29PWJgb7mTAgD5mbvDtEMDmX7n3NxcqcpT+1S/fv2EEA8ePKjncmdnZyFERkZGjfasrCzjWdPDaktNTY2Ojp4/f770Tdq+fftSU1OXLVv2xRdffPzxxxs3blSr1S25QACskq2tzSsBk13atRVCPMrL37XvRGWlXu6kAADNOP6p7uzZs3Fxcc8///yIESNMvOTq1atCiKFDh9ZoHzZsmKhWxjIxrDaFQqHX//d/iSIiIpRKZUBAgIm5AS3JxclxxviR0vGBkz8+ysuXNx8AsAQNLgDF/A/p66DCBjK9I7VavXPnzm3bthnHGUYFBQVCCCmBx5H2aK89gomPjxdCDBgwoEFhtQUHB7u7u0+aNEn6ePXqVTc3N39/f+mt+G7duk2cODE5Obm4uLj+xwTq59hW87vAqW3s1EKIu2mZYYfPtPxapwCAFhv/GOl0uq+//trR0fH11183/arc3FwhRMeOHWu0d+jQQQiRl5dneli3bt00Gs2+ffsePXokhNDr9Tt27DAYDNLYKTc399ChQzNnzuTtdVisZwb3GeDTXQhRXl4Rfji6ihEUgKdeg18Bk/Y+bwEODg6enp6ZmZmXLl2SpitLDAbDhQsXhBDe3vUt6T9mzJhvv/02ISFBp9MZZyZnZmYmJiZqNJqRI0c2KKyG5OTkCxcurFy50sbmf/8A61yAwLqXpUTL8HBzWTx78rdhR/T6ql9uJWtdnF58/hm5kwKAp0uLjX+Mtm3bVlBQ8M477zToBStpA9Pac47q3AW1/jC1Wv3mm29u2LAhKChIWgT6wYMHw4YNk/4owsPD1Wr1zJkzTUzMYDBIb8/VeYrx0tPG+G1WVVVVs/70Z04afTc9s7Co5G5a5vnLv/iNGNh8fQFAC2vEv58WvQuYv7//119//dVXX9nY2AwfPlylUuXn54eGhsbGxrZp02b69OnGyPDw8MrKykGDBvXv319qcXJy8vX1vXjx4qeffrpmzRqNRqPT6datW2cwGMaPHy+9t2V6WA3BwcEdO3YcP368sWXgwIFXr16NiIiYPXu2QqFITk4+efKkt7d37Q3mgUbw9uoYOG3cnoOnDUKcufjvdo4Ozw3tJ3dSAIDmEhsbGxMTM2zYsLFjxzboQql2U3v4UWcB6Ilh48aNc3Z2Dg0NvXPnjlarXbhwYWBgoEKhePjw4bFjx15++WWNxtTNlfLz82NiYmq329ra6nS6e/fumXgfWAfjtLisrCxlVXmz9jV6iM/xCwlCiONnLzuola5ODM4BWIlGvBpi0QWg6dOnX7p06Zdffvn73/+uUqkcHBzy8/OFEHZ2dn/4wx88PDyMkeHh4aWlpUql0lgAEkK88cYbiYmJCQkJixYt6tSpU2pqqsFg8PLyWrx4cfVeTAwzSkxMjI2NXbVqlUqlMjYGBATExMTs2LHj+PHjjo6OSUlJCoWi+margJmG9Ov5MCfv1IUrQoiDURecHR369uwqd1IAgKZXVFS0efNme3v7FStWGDdcN1H9Y8GKiooGhQkhhgwZYtwBzSgsLMzBwWHGjBmmJ6bRaIYPH167/erVqw4ODtUHdXgatGlzVzpwcXFp7p++h4dHxqOCX24l66uqYq7cXjr3RZXK3FVQAcASNPsMoC+++KKhHdTw1ltvmR6sVCrXrl0bFRV15syZjIyM4uJib2/vXr16zZs3z93d/YmXa7XaTZs2BQcHx8bGpqenu7u7+/n5zZ8/v02bNo0IM9q9e7eXl1eNqeB2dnbr16+XtoHPyMgYOnToq6++2rUrv5+jKU30G5GrK7xyLbHKYAg9eHr5opc6emjlTgoArF8Lj3+2b9+ek5Pz+uuvN+IXY2dn55KSktrbm0oTLqrvgmpKWJ0yMzOjoqKWLl1qb28vhCguLpbGP8XFxT4+Pq+88kr37t1rX2VraystMFTD9evXbW1tG71ONlop4yoK9vb2LfDTnz3l+ZT7D/LyCzMf5vyUcHvyGFNXVQcAS9bsBaA//OEPDe2ghgYNgIQQCoVi8uTJkydPrj8sLCysznYnJ6egoKCgoKD6LzcxTPLBBx/U2a7RaJYvX758+XJTbgI0gkKIOVOfzy8sunM3vay84vvwo79fPMupHTOZAaB5teT4JzEx8cSJEz4+PtVfdTeds7NzRkZG7cqO1FK9AGRKWJ1CQkKcnZ2nTJkihCgrK1u9enVqamqXLl1cXV3j4+Pj4+M/+uijPn36NCJ5oJnY26kDp437Zs9hg8EQ/VN8nx5eXh2ZdAbgacQESKA1USmVi2dN8nR3FULkFxZ/F360tKx535wHALSkrKwsIURiYuKsWbNe+h8ff/yxECIhIUH6eOfOncdd7uzsLITIyMio87bSWdPDaktNTY2Ojp4/f760TuK+fftSU1OXLVv2xRdffPzxxxs3blSr1Vu2bGnwYwPNrEfXjqOG9xdCVFVV7Tl4ury84omXAID1aVgByGC2ZnoM4OlhZ6deMudFRweNECIrOzd4/0k2TwGAZtWS4x9bW1vnWqS1llUqlfRRqXzs+E3aoz0hIaFGe3x8vBBiwIABDQqrLTg42N3dfdKkSdLHq1evurm5+fv7S2sVdevWbeLEicnJycXFxaY/MtAypo59tr27qxDiUV7+0bOxcqcDADKw6EWgAdTJxcnx1blTtgYfKK+oTPwtbd/x83OmPi93UgCAJjBy5MiRI0fWaLxw4cLHH388YMCAtWvX1n/5mDFjvv3224SEBJ1OZ9w/PjMzMzExUaPRGO9sYlgNycnJFy5cWLlypXEBF/GYBQj4ZgIWyMZGNW/6uM07I/VVVT9dud6nh1dv7y5yJwUALaqFXgHLzs6eNWvW6tWrW6Y7wOp18nR7eeZEpUIhhLj8y63on+LlzggAUFNzj3/Cw8NDQkKuX79ubHFycvL19S0pKfn000+laTg6nW7dunUGg2H8+PHSe1umh9UQHBzcsWPH8ePHG1sGDhyYk5MTEREhzXJKTk4+efKkt7d37Q3mAUvQqb3b+OeGCiEMQuw9era4tEzujACgRbXQDKAzZ87s379fq9WuX7++ZXoErF6fHl4zJ/vtOx4jhDh+NtbJ0WFo/15yJwUA+F/NPf4JDw8vLS1VKpX9+/c3Nr7xxhuJiYkJCQmLFi3q1KlTamqqwWDw8vJavHhx9WtNDDNKTEyMjY1dtWqVSqUyNgYEBMTExOzYseP48eOOjo5JSUkKhWLFihXN8bBAk3hh1NDbv6Wm3n+QX1h8IOrCAv8X5M4IAFpOExSAKisrP/vss+jo6F9//fVxAUlJSUIIW1tb87sDYPTskL4PH+Wdj7tqEOKHo2fbOTr08Oood1IA8FSw2PGPVqvdtGlTcHBwbGxsenq6u7u7n5/f/Pnz27Rp04gwo927d3t5efn5+VVvtLOzW79+vbQNfEZGxtChQ1999dWuXbs24+MB5lEqlfOmjfvn9xEVlZXxN+7069l1UN8ecicFAC3E3AKQXq/39/c/duyYKcFr1qwxszsANUx/YWRefuG1xN/0+qp/7YsKWjzT3fWxu7cAAJpEC49/Ro8efeDAgdrtYWFhdcY7OTkFBQUFBQXVf1sTwyQffPBBne0ajWb58uXLly835SaAJXDXOk8Z63vw1I9CiH3HY7p29nRydJA7KQBoCeYWgA4cOCCNfjp16jRhwoSCgoJ9+/YJIaZNm9alS5fCwsKzZ8+mpaUJIUJCQhYsWGB+xgCqUygU8/3H54cWp6RnFZeWbQ87umLxrLYOdX9/CwBoEox/gFZt1IgBt39LTUxOLSkr/+HI2aXzpynkTgkAWoC5i0B/8803QoiuXbsmJCTs2LEjIiJiyZIlQohnnnlmy5Ytu3fvvnfv3h/+8AchxK5du9gGHmgOtjY2S+a8qHVpJ4TI0RV8v/dYRUWl3EkBgDVj/AO0agoh5k55vo29nRDi17tpsfE35c4IAFqCuQWgO3fuCCF+97vfabVaqWXq1KlCiIsXL/53B0rlZ599NmLEiCNHjpw6dcrM7gDUyaGN/atzpmjs7YQQaRkPgw+cquL3DQBoNox/gNaunaOD/8RR0vHh0xezc3Xy5gMALcDcApA0vdnHx8fYMnr0aCHE7du3jS0KhWLlypVCiB07dpjZHYDHcdc6L57zoo1KJYS4eefe0ehLcmcEAFaL8Q9gBYb17zWwj7cQoryiMuxwNF+eAbB65haAbGxqriLUsWNHe3v7lJSUoqIiY+OgQYOEEDExMWZ2B6Ae3Tt7zps+TnqJPSb2lx9/viZzQgBgpRj/ANZh1mQ/RweNECIlPevcpQS50wGA5mVuAahz585CiOoboCqVyh49ehgMhvj4eGOji4uLECIzM9PM7gDUb1DfHpPGjJCOD526eOPXu7KmAwDWifEPYB0c2tgH/s+XZ1ExcemZ2TInBADNydwCUL9+/YQQX3/99aNHj4yNvXv3FkIcPXrU2JKUlCSEcHd3N7M7AE/0wqhhzw7tJ4SoMhhCDp5Ovf9A7owAwNow/gGshk/3ziMG9xFC6Kuq9hw+U1mplzsjAGgu5haApJfb09LShg0b9pe//EVqnDBhghBi8+bN169fF0IUFxd/+OGHQohevXqZ2R0AU7w0cVSv7p2FEBUVlTsjTuTmF8qdEQBYFcY/gDXxnzBK2k31QXZu1Pk4udMBgOZibgHIz89vzpw5QoiUlJS1a9dKjfPnz3dycsrNzR08eHC/fv06dOgg7X+xfPlyM7sDYAqVUvkfsyZ18NAKIQqKireHHy0pLZM7KQCwHox/AGuitrWZN22cUqEQQpyL/SU5NUPujACgWZhbAFIoFMHBwevXrx81apSdnZ3UqNVqt23bplKp9Hr9zZs38/PzhRDz5s0LDAw0N18AprFT2/4ucKpTu7ZCiAfZubsiTlTqmdIMAE2D8Q9gZbp29vTzHSSEMBgMPxyOLiuvkDsjAGh65haAhBBqtXrVqlUXLlyQBjqSefPmxcXFvfnmm5MmTXrttdeCg4NDQ0OVyiboDoCJ2rXVLJ07xd5OLYRITs0IP3KW3U0BoKkw/gGszOQxIzq4uwohcnQFh09flDsdAGh6NTcxNYdara7+cciQIV999VUT3h9AQ7V3d335pQnf7z1eVVWVcOOOu6vTxNHD5U4KAKwK4x/AOtioVPNmjP9i5z69vio24VbfXt369vCSOykAaEp8JQVYOR/vLgGT/aTjU+d//vlaorz5AAAAWKYOHlrjV2V7j5wtLC6RNx8AaFrmzgC6c+dOg+J79uxpZo8AGuqZwX2yc3VnLyUYhIg4es6prUPPbp3kTgoAWjHGP4C1GjdySOJvab+lZhQWl+w7FrN49mS5MwKAJmNuAaihO5saDCxCAshgyrhn8wuK/n3jjr6qaldkVNCilzzdXeVOCgBaK8Y/gLVSKBSB08Z+tn1vWXnF9V/v/vv6r0P7N+zvOwBYLF4BA54KCiHmTB3btbOnEKKsrHzH3uMFRcVyJwUAAGBxXJ3bTRv3rHS8P+pCXn6hvPkAQFMxdwbQwYMH62yvrKxMTU29fft2SEhITk7O9OnTv/nmG3t7ezO7A9BoNjaqJbMnb969PztHl6sr+P6HY8tffklt25QrwQPAU4LxD2DdfIf2u5GUcjsppbSs/IcjZ19bMF0hd0oAYD5zf/ebMWNG/QH/+Mc/3n333W+++Wbp0qWHDh0yszsA5tC0sV8aOHXzrv2FxSXpmdnB+0++Mnsy+xMDQEMx/gGsm0KIOVOe3/TdD8UlpXfupV/8+dqo4QPkTgoAzNXsv/i1a9fuq6++Gjt27NGjR7/77rvm7g5A/Vyd2706d4qtrY0Q4lZSSmTUBbkzAgArxPgHaO3atdXMnjJGOj4SfSkrO1fefADAfC3xzb+Njc2rr74qhNi+fXsLdAegfp07uM+bPk6hUAghYuNvnr98Ve6MAMAKMf4BWrsBPt0H9+sphKis1IcdOqOvqpI7IwAwSwu9+tG/f38hxK1bt1qmOwD1G9jbe8pYX+n48JmfriX+Jm8+AGCVGP8Ard2syX5O7doKIdKzsqMvxsudDgCYpYUKQHfv3hVClJWVtUx3AJ5o7LODRw3rL4QwGAx7Dp5JSc+SOyMAsDaMf4DWro2deu7U56UVoE/9eCU144HMCQGAGVqiAFRaWvrJJ58IIXx8fFqgOwAmmjFxVL9e3YQQFZWVO/Yez87VyZ0RAFgPxj+AdejVrfPIof2EEFVVVWGHoysqK+XOCAAaydxdwEJDQ+s5azAYUlJSduzYcfPmTSHExIkTzewOQBNSKhQL/V/YFnIoNeNBUUnp9rCjKxbPctCwXTEAPAHjH+CpMm38yF/vpWfn6B4+yjt+9vKMCc/JnREANIa5BaCFCxeaGOnh4fFf//VfZnYHoGnZ2tq8Mnvy5l2RufmFj/Lyd+878dqC6TYqldx5AYBFY/wDPFVsbW0W+k/YvCtSX1V14edrfXt27dG1o9xJAUCDtcQrYG3btvX3979y5Yqrq2sLdAegQRzban4XOLWNnVoI8VtaZtihMwa5UwIAK8D4B7AmnTzdxj47WAhhMBjCDp8pKWVtLwCtj7kzgDIyMuoPUCgU7u7uSmULrTYNoBE83FwWz578bdgRvb7ql1vJbi6XJz//jNxJAYDlYvwDPIUm+g1PvJuWlvFQV1B06NTFwOnj5M4IABrG3AKQp6dnk+QBQF7eXh0DXhzzw5GzQojTF//dztFBWu8QAFAb4x/gKaRUKudNH//59xEVlZU/X0vs09NrYG9vuZMCgAbgiykA/23EwN4vjBomHR88+WPib2ny5gMAAGBRPLTOk8aMkI4jj58vKCqWNx8AaBBzZwD9+c9/NvMOf//73828A4CmMmnMiLz8wivXEvVVVf+KjHpz0UsdPLRyJwUAFofxD/DUGvPMwFtJKckp94tKSiOOxSyZ86LcGQGAqcwtAH344Ydm3oEBEGA5FELMmfp8fkHRnXvpZeUV28OP/n7xLKd2beXOCwAsC+Mf4KmlUCgCp43dtH1vWVn5zTv34n65PWJQb7mTAgCTmFsAmj9/vhDi/Pnz6enpxkaVSqVSqcrLy40tdnZ2bdvyOyTQCqiUyv8ImLTlXwcyH+bkFxZ/98OxoEUv2dup5c4LACwI4x/gaebi5Oj/wnM/HD0rhDhw6sfuXTpoXdrJnRQAPJm5awCFhoaOHTtWGv0MHz48JCQkOTm5tLS0pKQkJSUlMjJy1KhRQgi1Wr179+7sujTBQwBoUvZ26iVzXmzr0EYIkfUwJ/jAqaqqKrmTAgALwvgHeMqNGNR7QO/uQojy8oqwI9FVBoPcGQHAk5lbAEpISFi5cqUQ4j//8z8vX768YMGC7t2729jYKJXKLl26zJw58/z583/5y18KCgrmzJmTmJjYFDkDaHYuTo6/mztVbWsjhEhMTt13/LzcGQGABWH8AyBg8hjp27J7aZkXLl+VOx0AeDJzXwHbuHFjZWWlUg5bqgAAIABJREFUn5/fRx99pFAoagcoFIq//vWvZ86ciYmJ+eSTT7755hszewTQMjp5ur08c+LOvcerDIbLv9xyc2k3duQQuZMCAIvA+Aeo3+WEWz/F33jc2bz8Qulg77Fzdmrbx4W9MvtFJ0eHpk+uiTho7Ge/OGZnxAkhxPFzl3t17+zp7ip3UgBQH3MLQKdOnRJCTJ06Val87GQipVI5derUmJiYqKgoM7sD0JL69PCaOWn0vhPnhRDHzsa2c3QY2r+X3EkBgPwY/wD1yy8sTs988quO2Tm6es5W6vVNl1Gz6Ner2/ABPj9fS6zU68MOnfn9KwEqlbkvWABA8zH3X6isrCwhRPv27esPc3d3F0JkZmaa2R2AFvbs0H6jRwwQQhiE+OHo2aSU+3JnBADyY/wDQOI/cZRLu7ZCiPsPHp268LPc6QBAfcydAeTi4vLgwYPLly+/9tpr9YRdvnxZCOHo6GhmdwBa3owXnsvLL7yeeFevr/pX5Mmg/3jJ3dVZ7qQAQE6Mf4D6jX128Kjh/c28SavYhNTeTj3P/4VtwQcNBsOZn+J7eXfp3tlT7qQAoG7mzgDy9fUVQoSGhtazwGFiYmJoaKgQYuDAgWZ2B6DlKRSKBf4veHVqL4QoLindHn6ssLhE7qQAQE6Mf4D62dio2tjbmfm/OhfYskDdO3uOHj5ACGEwGMIPR5eXV8idEQDUzdwC0Ouvvy6E0Ol0/v7+Fy9erB3w008/+fv75+fnCyEWLVpkZncAZGFrY7Nkzotal3ZCiJy8/O9/OFZRUSl3UgAgG8Y/AKqbMs63vburECInL/9o9CW50wGAupn7Cpi/v//8+fP37NmTmJg4atSoCRMmjBw5skePHkKIpKSkS5cunTx5Uor09fV99dVXzewOgFwc2ti/OmfK5t37S0rL0jIehh2OfnnmhNby1RwANC3GPwCqs1Gp5k8f9+XOSH1V1U//vtGnZ9fe3l3kTgoAajK3AKRQKHbu3Ono6Cjtb3rq1ClpX4waxowZExERYWNjbncAZOSudX5l9uRv9xyp1Ouv3k4+etZx2rhn5U4KwP9n787DojrPv4HfMwODgKzDvomoiAiIKyqoIC4gIqLgGjWmVGNsTGLMlSZN0itN38bEXFHTxETNhhoxoIiiLIoKIi5IFMSdVUHZl2GZAWZ7/5iW8gPEgTNwWL6fv45nnnOe77TV3tyc8zzAAtQ/ANCOlbmJz8yJSVf+UBCdiE95+0+hOsO02A4FAPB/qGGfQj6ff/DgwStXrqxevVogELT9yMTExMfH58iRI5cuXTIxMWE+FwCwa6StZWiAt/Kxn8s3sq7eusdyIAAAlqD+AYB25s6YaGtlRkR1DaJT59PYjgMA0J7afiXl6enp6empUCiKioqqqqqIyMHBwcDAQF33B4B+YsK4UZU1wvOpGUR0JumqoZ6u8xh7tkMBALAD9Q8AtOJyuSsCvL/5NVoikWbdzx0/eoTbuFFshwIA+B81PAHUFofDsbOzc3V1nTBhAqofgMHKd+Ykj4nORCRXKCJiLxaVlLOdCACATah/AEDJ1NjQf8405fHJxFRhXQO7eQAA2lJbA+jcuXMbNmwYOXKkrq6upqbm8+fPiSglJeWHH35Q/kIMAAaTJfNmjrG3ISKJRHoo+lwN6hsAGJJQ/wBAOzMmuzg62BKRuLnlePxlBdt5AABaqaEB1NzcvHbt2oULFx46dKiwsFAkErV+VFBQsGXLFisrq59++on5RADQf/C43FeWzrM0ExBRfYPol6h4cXML26EAAPoO6h8A6BSHKMRvtvYwLSLKKSxOv32f7UQAAP/BtAGkUCi2bNly9OhRIjI2Ng4ODm77qZGREYfDaWlpCQsL++c//8lwLgDoV7S0+BtD/Az0dImovLLmcPQ5mUzOdigAgL6A+gcAuqCvp7tk3kzl8ZlL1yurhezmAQBQYtoAunr16i+//EJEfn5+OTk50dHRbT8NCgp6+PDhjBkziOgf//hHfn4+w+kAoF/R19PdsHwhn69JRPlPn0fFJeM5ZwAYClD/AEDXJo4f4+bkQEQSiTQyLlmuQIkEAOxj2gD64YcfiMjMzOzYsWPGxsYdBzg6Op4/f97Ozk4ikXz55ZcMpwOA/sbK3GRt0Dwul0tEmfdzL169xXYiAIBeh/oHAF4qaIGXnq4OET19VpZyI4vtOAAAjBtAN2/eJKKNGzd2seeFrq7u5s2biej27dsMpwOAfmisg23wAi/lcVJqxq27j9nNAwDQ21D/AMBL6WoPCw3w5hARUVJqRnFpBcuBAGDIY9oAevLkCRG5uLh0PczR0ZGIHj58yHA6AOifpk5wmu0xgYgURCfiL+c+ecZ2IgCAXoT6BwBU4TjSZuoEJyKSyeWRZ5OlUhnbiQBgSGPaANLS0iKixsbGroeVlJQwnAgA+jl/bw9359FEJJPLD588X1pRzXYiAIDegvoHAFS02HemiZEBEZVX1py7ksF2HAAY0pg2gEaPHk1EGRkv+bcsJSWFiEaOHMlwOgDotzhEIf5zRthYEFFzc0v4icT6RtFLrwIAGIhQ/wCAiviaGqEB3lwOh4hS0+/kF6EvDACsYdoACgoKIqLw8PCcnJwXjTlx4sSJEyeIyN/fn+F0ANCfaWjwNixbYGJsQEQ1wvpfjye0SKRshwIAUD/UPwCguhHW5rOmuRGRQqE4fja5ubmF7UQAMEQxbQC9+eabJiYmEonE29v7woULcrm87afFxcVbt25dtWoVEeno6Gzbto3hdADQz+loD3st1H+4jjYRPSutPHoqCfueAsDgg/oHALpl/qwplqbGRFQtrD9z6TrbcQBgiGLaADI0NIyOjtbW1n7+/Pm8efPMzc2V5xcvXmxsbGxra7tv3z6pVMrhcMLDwy0tLRkHBoD+zthQf0PIQk1NDSJ6mPf01LkrbCcCAFAz1D8A0C0aPN6qJb4aGjwiupn18O7jArYTAcBQxLQBRESzZs1KT0+fNm0aEVVWVipPZmVl1dTUKI8dHBwuXLgQEhLCfC4AGBBsLc1WLPLmcDhEdCPzwZWMbLYTAQCoGeofAOgWcxMjX8/JyuOTiakNjWJ28wDAEKShlru4uLhcv3792rVrcXFxt27dqqyslEqlxsbG48ePnz9/vr+/P4/HU8tEADBQuDo5+NXWxaekE9HZi9cN9Ye7OGIZVAAYVFD/AEC3eHtMeJxfVFBU0ihqik5MXb9sAduJAGBoUU8DiIg4HM7MmTNnzpyprhsCwEA3Z7p7bX3jtVv3FArF77GX9Ffp2Fmbsx0KAECdUP8AgOo4HE5ogPfen483t0ju5xTeupczafwYtkMBwBCihlfAAABeJHDeTOfRI4hIIpWGn0isqqljOxEAAAAAa4wN9Bb5TFcenz6fVlvXwG4eABhS1PMEkEKhKCgoyMnJUbxsux8/Pz+1zAgAAwKXw1kVOPeHo7HPyyobxU2/nkjY8kqQzjAttnMBAKgB6h8A6AEP93EPcp88zHva1NwSFZcctmoxh+1IADBEqKEBdPXq1VdeeaWgQKWl7F9aIQHAIMPna24M8fvucExtXUNFVe3hE4l/WhWggXUxAGCAQ/0DAD223G/27p+Pi8RNeU+eX/3jrudkF7YTAcCQwPQVsPz8fB8fHxWrHwAYmvSG62wM9dfW4hNRQXFp5Nlk/CQEAAMa6h8AYEJvuM5yv9nK4/jkG2WVNezmAYAhgukTQJ9++mlLSwsRLVmyZPPmzfb29lwu1hUCgPbMTYzWLVvwU2ScTCa/8yDPxMhgwawpbIcCAOgh1D8AwNB4R3t359GZ93OlUlnkmUtvrF/Kwz8jANDLmDaAbty4QUQBAQExMTEcDl5fBYAXcrCzCl4w63h8ChFdvHrLYLiOx0RntkMBAPQE6h8AYC5ogVdhcWltXcOzsspL127P85zMdiIAGOSYtpkLCwuJ6M9//jOqHwB4qSluY+fOmKg8Pp10NaegmN08AAA9g/oHAJjT1uKH+M9R/iNy8ertopJylgMBwGDHtAFkZGRERAKBQB1hAGDwmz976qTxY4hIJpcfiTlfUl7FdiIAgG5D/QMAajHa3nr6pPFEJJfLI88mS6RSthMBwGDGtAHk4eFBRLdu3VJHGAAY/DhEyxfNGT3CmoiaWyS/RMUL6xrYDgUA0D2ofwBAXRZ5e5gaGxJRRVVtQko623EAYDBj2gDavn07h8P55ptvamqwdj0AqITH5b4SPN/c1JiI6hpEPx9PaGpuYTsUAEA3oP4BAHXR1NRYFThXuQL01Yy7j/GCPAD0GqYNoNmzZ+/evTsvLy8gICAzM1MtmQBg0Bumxd+wfOFwXW0iKquojjh9QS6Xsx0KAEBVqH8AQI2sLUzmTHcnIgXR8fgUcVMz24kAYHBiugtYTEzMiBEjNm/evH///okTJ3p6eo4dO9bW1lZDo/M7f/TRRwxnBIDBwdhA79UQvwNHY1sk0kf5RSfPXVnuN5vtUAAAKkH9AwDqNc9z0uOCouKSirr6xtgL11YEeLOdCAAGIaYNoODg4LZ/TEtLS0tL62I8CiAAaGVjYbo6aN7hE4lyheJm1kMTI4M5HhPYDgUA8HKofwBAvbhc7ooAn3//Gi2RSm/dfTxulJ2rkwPboQBgsGHaANLV1VVLDgAYmsaNslvkM/3MxWtElJB8Q19Pd6LzaLZDAQC8BOofAFA7M4HhgtlTzl68TkQx567Y21ro6eqwHQoABhWmDaCGBmzfAwCMeE11rRHWp/1xV0F0Ij7FWH/4CBsLtkMBAHQF9Q8A9AavKa4Pc5/mPX3eKG6KTkjdsHwh24kAYFBhugg0AABzi31njHe0JyKpVBYefa6yWsh2IgAAAIC+xuFwViz20dbiE9GD3Cc37zxkOxEADCpoAAEA+zgczqrAuXZWZkQkEjf9HBXfIBKzHQoAAACgrxno6Qb4zlAexyZdraqpYzcPAAwmaAABQL+gqaGxYbmfwEifiKpr68KPJ0okUrZDAQAAAPS1Ka5jXcaOJKIWiTQyLlmuULCdCAAGiW6vAeTu7q48SEpKMjEx+ec//9mty7ELBgC8iK7OsA3L/b4/ckrc1FxUUh55NnlNkC+Hw2E7FwAAC/XPw4cPjx8//vTp09raWmtra2dn51WrVunp6alyrVAojIiISE9Pr62tFQgEM2bMWLVqlY5O+9VkVRzWtaamprCwME9Pzy1btnTrQgDoQvCCWYXFpQ2N4ifFpVduZs+e5sZ2IgAYDDiKbnaUW38YKykpsbCw6O7PZt2dbuhISkqysbFxcnJiOwj0qejE1PTMB0S0eU3gSFtLtuP0CwVFJT/9HieVyYhojscEf28PthMBQH8nl8vj4uKIyM3Nzc7Orjem6OP6JzY29scff1QoFFwuV09PTygUEpGBgcHOnTutra27vraqquq9996rrKwkomHDhjU1NRGRjY3Nl19+OXz48O4Oy8rKOnbsWH5+vomJiZeXV0hIiKamZtvpoqKijh07duDAAYFA0K3vqIT6B+BF7ucUHoo+R0QaPN7WDcGWpsZsJwKA/qUH9Q/TV8B0u4nhdAAw6I20tQwN8Fb+aJVyI+varXssBwIA6KBX65+qqqrw8HAieu2116Kiog4fPvzjjz+OGzdOKBTu27fvpb2kAwcOVFZWOjg4fPfdd7///vvu3bvNzMyKi4sPHTrU3WHJyckff/xxXl7e6NGjm5ubIyIiPvvss7YBGhsbo6OjFy1a1LPuDwB0wXmM/WRXRyKSymRRZy7JZHK2EwHAgNftV8BSU1OVB8bGxoRtUAGgF0wYN6qyWnj+SgYRxSZdNdDTdR5jz3YoABjS+rL+OXv2bEtLi6en59KlS5VnzMzM3n333c2bN2dnZ1dWVpqamr7oWqFQmJ6ezufzP/jgA3NzcyIaNWrUhx9++Pbbb1++fDksLIzP56s4rKWlZf/+/RYWFjt37jQ2NpbJZAcOHIiPj7969aqnp6dyupiYGKlUGhIS0nv/aQAMZUt8Z+Y/eV5T1/C8vCop7Y+Fs6eynQgABrZuPwHk9V/KAgIAoDf4ek5S/tZLrlBExF4sKilnOxEADGl9Wf88ffqUiGbOnNn2pJmZmfLlr6Kioi6uTU1Nlclkbm5uyraOkoODg4ODg0gkysjIUH1YYWFhY2NjcHCwsufF4/E2bNhARHfv3lWOr6urO3XqVGBgoIGBgTq+NwC0p6XFXxk4V/nOafL1zILiUrYTAcDAhl3AAKCfWuY3e4y9DRFJJNJD0edq6/C8IQAMCUKhUEdHp21rRklLS4uIlIv1vEh2djYRTZw4sd35SZMmEVFWVla3hnXE4XBkMpnyODo6msvlBgcHv+T7AAAD9jYWnlNciEihUESdTW5ukbCdCAAGMDSAAKCf4nG5ryydp1zysL5B9HNUvLi5he1QAAC9bteuXceOHXN0dGx7sr6+vri4mIhGjBjRxbU1NTVEZGVl1e68paUlEdXW1qo+zN7eXkdH5+TJk1VVVUQkk8nCw8MVCoWrq6vyDmfOnAkKClJxYzIA6DG/OdMsTI2JqLq2Lj75BttxAGAAQwMIAPovLS3+xlB/Az1dIiqvrDkcfQ4rIALAkCKRSKqrq//444/PPvtMLBbPmjWr613AlPuFdVx2Wrmxl/JTFYfx+fzXX3+9tLR0y5Ytf/vb3zZv3hwfHz9p0iQvLy8iioqK4vP5QUFBavmaANAFDR5vRYA3j8clohu37z/Ke8p2IgAYqLq9CDQAQF/S19Ndv3zh/qOxLS2S/KfPTyamhiyaw3YoAIA+smPHjoKCAuWxn59fWFhY1+OVvZu2+7grddoAeukwb29vQ0PDY8eO5ebmCgSC1atXh4aGcjicioqKhISENWvW6OjoqPhFmpublU8wtSOXy5ubm1tnBIBO6Q7TnDnJOfXmXQVRVFxy2Ao/7WFabIcCAJbJ5d3+1TgaQADQ31mbm6wNmhd+IlEul2dkPzI21Js7cxLboQAA+sKIESMUCkVZWZlYLE5NTXVwcPDz8+tifNebxEskkm4NIyJ3d3d3d/d2AyIjI3V1dRcvXvyS9G00Nzfn5uZ2PK9QKJqamlrfTQOAF3Eeafkov6i8StggajqddG2hV/u/mAAw1HT9/+adQgMIAAaAsQ62Sxd4RSdcJqLzqRmG+sMnuTi+9CoAgIFu+/btRKRQKFJSUr755pt9+/YZGRl5eHi8aLyhoaFYLG5sbGx3XrlvvZGRUbeGdaq0tPT8+fOvvfbasGHDiEgkEh0+fDgjI0MkEjk6Oq5fv37kyJEdr9LX11+4cGHH80lJSQYGBl0vbAQAShtCBHt/OdHSIskvLqsVSSeMG8V2IgBgk1wub92aU0VYAwgABoZpE5xmT3MjIgXRifjLuU+esZ0IAKCPcDgcb2/vpUuXElFCQkIXIw0NDYmoY2dHeaZtA0iVYZ2KiIgwNDRUPojU3Nz83nvvnT17ls/n29jYZGZmbt++/eHDh935cgCgKoGhvt+cacrjmMRUITZIBYBuQgMIAAYMf5/p7s6jiUgmlx85eb6soprtRAAAalZTU3P8+PFOuzzOzs5EVF5e3sXlys5OSUlJu/NlZWWtn6o+rKOioqLk5OSVK1fy+XwiOnnyZFFRUVhY2LfffvvFF1/s3r2bz+f/8MMPL/mSANBTMyaNH+tgS0Ti5pbj8Ze7/foHAAxtaAABwIDBIQrxnzPC2pyImppbfj2R2NAoZjsUAIA68fn8Q4cOHThwQCaTtfuovr6eiIyNjbu4XLlHe1ZWVrvzmZmZROTi4tKtYR0dPXrU1NR0/vz5yj9mZ2ebmJgEBgZyOBwisre3nzdvXn5+vkgk6vprAkDPcIiW+83WGaZFRDmFxTdu32c7EQAMJGgAAcBAoqHB27B8oYmxARHVCOt/PZ7QIpGyHQoAQG10dXUtLCykUumNGzfanlcoFGlpaUTk4ODQxeWzZs3i8XhZWVlt99UqLS19/Pixjo7O9OnTuzWsnfz8/LS0tFWrVmlo/G8RyU63IOnBviQAoCJ9Pd3A+Z7K47OXrldWYxM9AFAV0wZQ2H8pfyvVKaFQ2DqM4XQAADrawzaG+uvqDCOi4tKKo6eS5N1fAB8AgIlerX8CAwOJ6Pvvv09PT1c+B1RXV3fw4MH09HRtbe2AgIDWkVFRUREREffu3Ws9Y2BgMG3aNLFY/NVXXykfwxEKhTt37lQoFD4+Psr3tlQf1s7Ro0etrKx8fHxaz7i6ulZXV0dHRys3IsnPz09KSnJwcOi4wTwAqNFE59FuTg5EJJFII+OSUQgBgIo4Pdg57P9cz+EoDyoqKkxMTDodU1paamlpqTxmON0glpSUZGNj4+TkxHYQ6FPRianpmQ+IaPOawJG2lmzHGUgKi0t//P2sVCojIq8prot9Z7CdCABYI5fL4+LiiMjNzc3Ozq4PZuzV+kcul3/yySd37twhIh6Pp6urW1dXR0RaWlpvvfWWl5dX68gVK1Y0NTWtXbt25cqVrSerqqp27NhRVVXF4/Gsra2LiooUCoWdnd2XX36po6PT3WGtHj9+vGPHjh07dsyePbv1ZHNz8/bt24uKiiwsLPT09PLy8jgczhdffOHoqOpGjah/AHpG3NS85+fjwvpGIlo4e6rPjIlsJwKAvtaD+gevgAHAgGRvY7EywEf5M9iVjOy0jO7tgAgA0G9xudzPPvvsL3/5y/jx4w0MDFpaWhwcHBYuXLhv37623Z8XEQgEe/bs8ff3NzQ0fPbsmamp6bJly3bt2tWuraPisFZHjhyxs7NrF0BLS2vXrl0BAQEKhaKkpGTixIl79uxRvfsDAD2mPUwrxH+OshV9/sofxaUVLAcCgIFA4+VDurRr1y7lwYvKBSLS09NrHQYAoC6uTg4La+sSUtKJ6MzFa4b6w8c72rMdCgCGhN6ufzgczoIFCxYsWND1sMjIyE7PGxgYbNmyZcuWLV1fruIwpX/84x+dntfR0dm8efPmzZtVuQkAqNGYkTZT3celZz6Qy+WRZ5Pf3BCsqcH0hzsAGNyY/huxY8eOl47R1dVVZRgAQHd5T3cX1jVcu31foVAci73459WL7azM2A4FAIMf6h8A6A8Wz52R/+R5ZY2wvLLmfGrGIp/OV3AHAFAaAE3iysrKo0eP5uXllZSUCAQCBweH0NBQe3t7Va4VCoURERHp6em1tbUCgWDGjBmrVq3q+Ms6FYd1rampKSwszNPTU8XfpAGAWgTO96ytb3yQ+0QilYYfT3hj3VKBkT7boQAAAAB6HV9TIzTAe/9vp+UKRerNbKdRdg52VmyHAoD+q7+vAXT79u2tW7cmJSUVFBRoaWk9f/48NTX1nXfeOXv27Euvraqqeuedd+Li4iorKzU0NMrKymJiYnbs2NHQ0NCDYVlZWR988MHKlSu3bt0aEREhkUjaTRcbGysWi1esWMH8WwOA6rgczqrAuVZmAiJqFDeFn0gQNzWzHQoAAACgL4ywNp/lMYGIFApFVFxKc3ML24kAoP9SzxNACoWioKAgJyfnpZtc+Pn5qX5biUTy/fffi8XimTNnvvHGG/r6+iKRKDIyMjo6+scffxw/fnzXzwEdOHCgsrLSwcHh3XfftbGxyc/P//zzz4uLiw8dOvTGG290a1hycvLXX389bNiwMWPGlJWVRUREPHjw4NNPP23dBKSxsTE6OnrRokUCgUD1LwgAaqHF13w11H/f4ZjauobyqtpD0ef+tHKRBo/Hdi4AGOR6qf4BAOiW+V6TH+cXlZRX1Qjrz1y8vtx/9suvAYAhSQ0NoKtXr77yyisFBQWqDO7WNqgpKSmlpaUmJibvvvuupqYmEeno6Lz66qvV1dXJyclRUVHvvffei64VCoXp6el8Pv+DDz4wNzcnolGjRn344Ydvv/325cuXw8LC+Hy+isNaWlr2799vYWGxc+dOY2NjmUx24MCB+Pj4q1evenp6KqeLiYmRSqUhISGqfzsAUCP94TobQ/1/OHJK3NxSUFQSdTZ51RJfDtupAGAQ6736BwCgWzR4vFWBc/8dHi2Vym7eeTh2lK2L40i2QwFAf8T0FbD8/HwfHx8Vq58e3JyIfH19ld2fVr6+vq2fvkhqaqpMJnNzc1O2dZQcHBwcHBxEIlFGRobqwwoLCxsbG4ODg42NjYmIx+Nt2LCBiO7e/c+203V1dadOnQoMDDQwMFDH9waAnjA3MXpl2QIej0tEWQ/yklIz2E4EAINWr9Y/AADdZW5iNN9zsvL4ZGJqQ6OY3TwA0D8xfQLo008/bWlpIaIlS5Zs3rzZ3t6ey1XbukLPnj0jIjs7u3bnDQ0NiaiioqKLa7Ozs4lo4sSJ7c5PmjQpPz8/Kytr5syZqg/riMPhyGQy5XF0dDSXyw0ODlbtawFAbxllZxW8YNbx+BQiunD1lv5wHY+JzmyHAoBBqFfrHwCAHpjtMeFRflF+UUmjqCk6MXX9sgVsJwKAfodpA+jGjRtEFBAQEBMT07ogjrps3LhxzZo1tra27c7n5OQQUdtndjqqqakhIiur9svgW1paElFtba3qw+zt7XV0dE6ePDlt2jSBQCCTycLDwxUKhaurq/IOZ86cWb58uZ6eXg+/JwCozxS3sVW1dZeu3Sai00lXjY30x9jbsB0KAAabXq1/AAB6gMPhhAR47/35eHOL5H5O4a27jye5OLIdCgD6F6a/rSosLCSiP//5z71R/djb248dO7bdduwNDQ2RkZFE1Lr+TqeEQiER6erqtjs/fPjw1k9VHMbn819//fXS0tItW7b87W9/27x5c3x8/KRJk7y8vIgoKiqKz+cHBQX1/HsCgFotmD110vgxRCSTy4/EJJVUVLOdCAAGm16tfwAAesbYQC/AZ7ry+HTS1Zq6hq7HA8BQw/QJICMjo9LS0j7b+ur58+dffvllaWmpsbHx4sWLuxip7N0o+zhtddoAeukwb29vQ0PDY8eO5ebmCgSazHzNAAAgAElEQVSC1atXh4aGcjicioqKhISENWvWtOtSdaGlpaW0tLTjeYVC0dLSUl9fr+J9YHCQSCTKA5FIhP/21WiB16RqYV1hcVlzc8vPkXGvLpuvP1zVv6QAMODI5fI+nrGP6x8AABVNcx93P/fJw7ynTc0tkbEXN60JRJ8aAFoxbQB5eHicOnXq1q1bysdheo9EIjl58mRkZGRLS4uent4nn3yir6/fxfiut9to/albxWFE5O7u7u7u3m5AZGSkrq5u162odsRi8Z07dzqe19TUFIlElZWVqt8KBoGmpiblgVAorORj/Qh1mjNlXK2woba+sb5BFBF7KchnioYGNoYHGJz6fo+tPqt/AAC6K2TRnD0/HW8QiQuKS6/+cc9zigvbiQCgv2DaANq+ffvp06e/+eabdevWGRkZqSVTR0+ePNm1a9fTp0+JyNXV9Z133jExMen6EkNDQ7FY3NjY2O58Q0MDEbVGVXFYp0pLS8+fP//aa68NGzaMiEQi0eHDhzMyMkQikaOj4/r160eO7GT/RX19/YULF3Y8n5KSYmBgMGLEiK6/Fwwywx8VKQ/Mzc1H2FqyG2bwCTMz/+G30w2N4sqautTbj9cvW4BVWgEGJblcfv/+/b6csW/qHwCAHhiuox3sN+tw9Dkiik++MXqElbmpMduhAKBfYNoAmj179u7du99+++2AgIB9+/Z1fEaGucTExAMHDkgkEiMjo40bN86ZM0eV5xgNDQ1LSko6dnaUZ9o2gFQZ1qmIiAhDQ0M/Pz8iam5ufu+994qKimxtbY2NjTMzMzMzMz///HMnJ6d2V3E4nHa72rf9CD+dDjWt/2Pmcrn4b1/tTIwMXg3xO3A0tkUifVxQfDrp6jK/2WyHAoDBoA/qHwCAHhs/xn6i8+jb93OlMlnk2eQ31i/loc4EAOYNoJiYmBEjRmzevHn//v0TJ0709PQcO3asra2thkbnd/7oo4+6df+UlJTvvvuOiGbMmPHWW2+pvtSOcqv4kpKSdufLyspaP1V9WEdFRUXJycmvv/46n88nopMnTxYVFYWFhQUGBnI4nMLCwvfff/+HH37Ys2ePioEBoDfYWJiuDpp3+ESiXKFIz3poYmQw22MC26EAYMDr7foHAIChoAVeBcWltXUNz8oqL129Pc9rMtuJAIB9TBtAwcHBbf+YlpaWlpbWxfhuFUAlJSXffPMNEa1cuXLNmjXdWsDM1dX12rVrWVlZAQEBbc9nZmYSkYuLS7eGdXT06FFTU9P58+cr/5idnW1iYqLs/hCRvb39vHnzYmNjRSKR6k0rAOgN40bZ+ft4nL14nYjik2/o6+m6O49mOxQADGy9Wv8AADA3TIsfsmjOT8fOKoguXrs91sHW1sqM7VAAwDKmjwLqdlO3bp6YmCiRSHx9fdeuXdvd5etnzZrF4/GysrJad/IiotLS0sePH+vo6EyfPr1bw9rJz89PS0tbtWpV21/0dboFSd/vSwIAHc2a6jZzsgsRKYiOx6c8eVbGdiIAGNh6tf4BAFCL0SOsZ0waT0RyuTwyLlkikbKdCABYxrQB1NBN3br5lStXiMjDw6OmM21bNlFRUREREffu3Ws9Y2BgMG3aNLFY/NVXX4lEIiISCoU7d+5UKBQ+Pj7K97ZUH9bO0aNHraysfHx8Ws+4urpWV1dHR0crNyLJz89PSkpycHDouME8ALAi0HfG+DH2RCSVysJPJFZWC192BQDAC/Vq/QMAoC7+3h6mxoZEVFFVm5CSznYcAGAZ01fAeo9EIikvLyeif/3rX50OMDMz+/HHH5XHUVFRTU1NXC53/PjxrQM2bdr0+PHjrKystWvXWltbFxUVKRQKOzu7devWtb2PisNaPX78OD09fceOHTze/7aUDg4OTk1NDQ8PT0xM1NPTy8vL43A4b7zxBsP/EABAXTgczqolcw9GnHn6vFwkbvo5Kv6NdUHDdbTZzgUAAADQWzQ1NVYFzt13OEYml1/94+5YB1tHB1u2QwEAa/rvavAVFRUM7yAQCPbs2ePv729oaPjs2TNTU9Nly5bt2rWr3aI8Kg5rdeTIETs7Oy8vr7YntbS0du3aFRAQoFAoSkpKJk6cuGfPHkdHR4ZfAQDUSFNDY8NyP4GhPhFV19aFH0/Es9AAAAAwuFlbmHhPdyfli/AJl8VNzWwnAgDWcJSvLDEnFAp/++23S5cuZWdn19TUSKVSY2Pj8ePH+/r6rlu3rovttEApKSnJxsam47bxMLhFJ6amZz4gos1rAkfaWrIdZ0gor6r9/sgpZfXj6uSwZolvd5cYA4D+Ri6Xx8XFEZGbm5udnV1fTo36hyHUPwB9QC6X7ztyqrikgogmjR+zYrHPSy8BgP6vB/WPGp4AUigUBw4csLGx2bp16/Hjxx89elReXl5dXZ2bm3vq1Klt27bZ2toePHhQXZ0mAAAmzASG65ct0ODxiCj7YX7C5ZtsJwKAAQn1DwAMFFwud0WAj6aGBhHdupdz52E+24kAgB1qWANo586dH374ofJYX1/f0dHR3t5eQ0OjoKDg4cOHQqGwoaFh06ZNdXV17777LvPpAAAYGmlrGRrgfez0BQVRyvVMQz1d5R4ZAACqQ/0DAAOImcBw4eypZy5eI6JT566MtLXQ0+18vQsAGMSYPgGUnZ390UcfEZFAINi7d29JScnNmzeVe3Jdv369pKRk7969AoGAiN5///1Hjx6pITIAAGMTxo3y9ZysPI5Nuno/9wm7eQBgYEH9AwADjucUl1F2VkTUKG6KOpuMpxMBhiCmDaBvv/1WLpdramqePn1627Zt7RZO1tbW3rZtW0xMjIaGhkwm++abbxhOBwCgLr5ekye7OhKRXKH4Pfbi87JKthMBwICB+gcABhwOh7NisY/2MC0ielxQnJH1kO1EANDXmDaALl26RETr1q2bOXPmi8Z4eXlt2LCBiJKSkhhOBwCgLhyiZX6zx9jbEFFzi+SX4wm1dQ1shwKAgQH1DwAMRAZ6uovnzlAex164WlVTx24eAOhjTBtAz549IyIPD4+uh02fPp2IiouLGU4HAKBGPC73laXzLE2Niai+QfRLVLy4uYXtUAAwAKD+AYABarKro+tYByJqkUgj45LlWKgeYChh2gBS7m3x0h0usMsyAPRPWlr8jaH+Bnq6RFRWWXMk+pxMJmc7FAD0d6h/AGDgWrrQS7kC9JPi0ivpd9iOAwB9h2kDyNramohu3LjR9bDr168TkY2NDcPpAADUTl9Pd/3yhXy+JhHlPX1+8lwq24kAoL9D/QMAA5eu9rBgv1nK43OpGSUV1ezmAYA+w7QB5OvrS0SHDx++fPnyi8akpaWFh4cT0dy5cxlOBwDQG6zNTdYu8eVyuUSUcefRxWu32U4EAP0a6h8AGNCcR4+Y4jqWiKQy2bHTF6RSGduJAKAvMG0Abdu2jcvlSqXSpUuX7t69u7Gxse2nIpFo7969QUFBEomEy+W++eabDKcDAOglY0fZLZ3vqTw+f/nmrXs57OYBgP4M9Q8ADHRL5s0UGOoTUVllzYW0P9iOAwB9gWkDyNnZ+YsvviCimpqa7du3W1paTp06NSQkJCQkZOrUqRYWFm+//XZVVRURff75587OzmqIDADQO6a5j5s11Y2IFEQn4lNynzxjOxEA9FOofwBgoOPzNUMDvLkcDhEl38gqKCphOxEA9DoN5rfYsWOHQCB466236uvr6+vrMzIyMjIy2g7Q09P7+uuv//SnPzGfCwCgVy3y8aipq7/7qEAmkx85eX7L2iXmpsZshwKA/gj1DwAMdPY2Fp5TXFNv3lEoFFFxKW9tXK7F12Q7FAD0IjU0gIho48aNy5cv/+233y5evHj37t2amhoiMjIycnFx8fHxWbt2rYGBgVomAgDoVRwOZ+Vin/oG0ZNnZU3NLeEnEt9Yt3S4rjbbuQCgP0L9AwAD3cI5U3MKi0srqqtr6+KSbwQv8GI7EQD0IvU0gIhIX19/y5YtW7ZsUdcNAQBYoamhsX75wu8Pn6qsEVYL6389nrBpTSBfU23/WgLAYIL6BwAGNA0eb0WA93eHY2Qyefrt++NG2TmNsmM7FAD0FqZrAAEADD662sM2rvDX1RlGRMWlFRGnkuQKBduhAAAAANTPytzEd+YkUq6BmHBZJG5iOxEA9BY0gAAAOiEw1F8XvEBDg0dED/Kexl26znYiAAAAgF7hPd3dzsqMiOobRCcSLrMdBwB6S7dfanB3d1ceJCUlmZiY/POf/+zW5R999FF3ZwQAYIW9jcWKAJ+I0xcUCsWVm9lGBnqek13YDgUA7ED9AwCDGJfLXRk4d+8vJ1paJPceF2bdz53gPJrtUACgft1uAGVlZSkPpFIpEX388cfduhwFEAAMIG5ODlW1dYkp6UR05sI1Q73h4x3t2Q4FACxA/QMAg5vAUN9/zrRT59OIKObcFXsbCwP94WyHAgA1Y7qsqa6urlpyAAD0Tz7T3evqGq7dvq9QKI7FXvzz6sXKZ6QBYChD/QMAg8/0SeMf5j19lF8kbm6Jik/508oADtuRAEC9ut0ASk1NVR4YGxsTUUNDg5oTAQD0M4HzPWvrGx/kPpFIpeEnEt54ZanASJ/tUADQp1D/AMCgxyFa7j9nz09Roqbm3MJn12/fnzHRme1QAKBO3V4E2uu/+Hx+bwQCAOhvuBzOqsC5VmYCImoUNYWfSBA3NbMdCgD6FOofABgK9IfrLJnvqTyOu3S9slrIbh4AUC+mu4B99dVXX331lVwu73rY06dPv/rqq/DwcIbTAQCwQouv+Wqov/Jl+PKq2kPR56QyGduhAIA1qH8AYLBydx7tNm4UEUkk0ojYC7KX/UMHAAMIR6FQMLqewyEiiUSiodHV22S3bt2aPHmytbV1cXExk+kGsaSkJBsbGycnJ7aDgJrdzHp4PfP+iz6trWtoFDURkYmxgRZf80XD1i9baKCH9SbYV1ZZ8/2RU03NLUQ0YdyoVUt88W48QH8gl8vj4uKIyM3Nzc7Org9mRP2jLqh/APohcXPLnp+ihPWNRLRg1pS5MyexnQgAOtGD+ofpItCqkMlkKSkpRFRRUdEH0wH0K3UNomellS8d1vUTtnjYpJ8wNzFaEzTv1+MJcrk860GeqbHhPK/JbIcCgH4K9Q8ADFDaWvyQRXN+/j1OQZSUdstxpK2NpSnboQBADXrSADIxMWl3xtzcXPmrsE6JxWKRSERE1tbWPZgOAKD/cBxpE7zQ60T8ZSK6kPaHkYHeZFdHtkMNIRl3Hj0tKWdyBw6HE7zAS115YEhB/QMAQ8cYe5tp7uNuZD6Qy+WRZy+9+eoyzS4feASAAaEnf42rqqranamurlblwr/85S89mA5gQPP1nOTriedmB5Wpbk7VNXWXrmcqiKITLuvr6Yyxt2E71FCR9/T57Xs5TO7ARQMIegr1DwAMKQFzZ+Q9eV5ZIyyvqj2XmhHgM53tRADAVE8aQH//+99bjz/99FMi+vjjj7ncrtaT1tTUnDZt2vz583swHQBAf7NgzrTa+sbb93JkcvmRmKTX1y6xNDVmOxQA9C7UPwAwpPA1NVYEeP/w22m5QnHlZva4UXYOdlZshwIARvpoEWh4KSyCCDCwyGTynyPj8p4+JyJ9Pd2t65Zioe4+UFZRXd8oftGncZeuPy+vIqK1S+Zpa2t1PohDo0fgfZzBpt8uAg0vhfoHoJ9LSElPvp5JREYGem9vXK6lxWc7EQD8BwuLQG/evJn+WwYBAAwdPB53bfD87w+fqqiuratvPHQicfOaQP6Lt3IDtTA3NTZ/8TKU2sP+0/Sxt7XQG67TR5lgSEL9AwBDxPxZU3IKi5+VVtYI62MvXgvxn8N2IgDoua6eW1aFi4uLi4vLxYsX1ZIGAGAA0RmmtXGF/3BdbSJ6Vlb526kkuVzOdigA6AuofwBgiOBxuSsDfDQ0eESUcefR3UcFbCcCgJ5j2gB6++2333zzzS+++EItaQAABhZjA71Xl/tpamoQ0aP8ophzV9hOBAB9AfUPAAwdZiZG872mKI9PnkttePG72ADQzzFtAI0bN46IcnNz1REGAGDgsbE0XbPEl8vhEFF61sPU9DtsJwKAXof6BwCGlNnT3BxsLYmoUdQUnZjKdhwA6CGmDaCPP/6YiJ48eXL27Fl15AEAGHjGjR7h7+2hPI67dD3zPn4mBBjkUP8AwJDC4XBCAry1+JpEdD+n8I+7j9lOBAA9wbQBtGLFij179nC53E2bNqWkpKglEwDAgDNrmtvMyS5EpCA6Hp/y5FkZ24kAoBeh/gGAocbYQC9g7gzl8emkqzV1DezmAYAeYLoLWEJCwtixY//2t7/961//8vb2njJlyvjx4y0tLXV1O98O+aOPPmI4IwBA/7TYd0ZtXcP9nEKpVBZ+IvGNdUEmRgZshwKAXoH6BwCGoGkTnB7kFD7Ie9rc3BIZe3HTmkBshggwsHAUCgWj67v5d57hdINYUlKSjY2Nk5MT20EAoOckEumBY2eKnpcTkcBQ/411S3V1hrEdagg5GHEm7+lzIvrb1lewDfyQIpfL4+LiiMjNzc3Ozq4PZkT9oy6ofwAGlgaReM9PxxtEYiJa7DvDa4or24kAhq4e1D9MnwB60W+6AACGIE1NjfXLFuw7FFNT11BVW3f45LmwlQHKnVMBYDBB/QMAQ9NwHe1gv1mHo88RUUJy+pgR1uamxmyHAgBVMW0ANTTg5U8AgP/R09XZGOr//W+nxU3NhcWlv5+9tGaJLx6QBhhkUP8AwJA1foz9xPFjbt/Lkcpkv59N3rpuKY/HdGFZAOgb+LsKAKBmZiZG65Yt0ODxiCj7YX7i5ZtsJwIAAABQm6D5nob6w4noeVnlxWu32I4DAKpCAwgAQP0cbC1DF81RPvaTfD3z2u37LAcCAAAAUJNhWvyQ/9Y5l65lKlc/BID+T20NIKFQuG/fvtDQUCcnJ3Nzc4FAMGbMmKVLl/773/+ura1V1ywAAAPFBOfRvp6Tlcex59Me5D5hNw8A9AbUPwAwNI0eYT1jsgsRyeXyY7EXW1okbCcCgJdTQwNIoVAcOHDAxsZm69atx48ff/ToUXl5eXV1dW5u7qlTp7Zt22Zra3vw4EHsfwEAQ42v1+TJLo5EJFcojsVefF5exXYiAFAb1D8AMMQt8vYwNzEioqraugS88A4wEKihAbRz587NmzcrV0PU19efMmVKSEjIqlWrPDw8DAwMiKihoWHTpk1ff/0187kAAAYQDtEy/9mj7a2JqLlF8mtUfG0dFo4FGCRQ/wDAEKehwVux2IfH5RLRtT/uPsovYjsRALwE0wZQdnb2Rx99REQCgWDv3r0lJSU3b96MioqKiIi4fv16SUnJ3r17BQIBEb3//vuPHj1SQ2QAgIGDx+WuWzrfwtSYiOoaRL9ExYubW9gOBQBMof4BACAia3MT7xnuRKQgOpFwWdTUzHYiAOgK0wbQt99+K5fLNTU1T58+vW3bNh0dnbafamtrb9u2LSYmRkNDQyaTffPNNwynAwAYcLS0+BuWL9TT1SGissqaI9HnZDI526EAgBHUPwAASr4zJ9lamhFRXX1jbNJVtuMAQFeYNoAuXbpEROvWrZs5c+aLxnh5eW3YsIGIkpKSGE4HADAQGRnovRrix9fUIKK8p89jzl1hOxEAMIL6BwBAicvlrgjw1tTQIKLb93LuPMxnOxEAvBDTBtCzZ8+IyMPDo+th06dPJ6Li4mKG0wEADFDWFiZrguZxuVwiunnn4aVrt9lOBAA9h/oHAKCVqcBw4ZypyuOYc1fqG0Ts5gGAF2HaAFLubfHSHS44HA7DiQAABjqnUXZL53sqj89dvnnrXg67eQCgx1D/AAC05TnZZdQIKyISiZsi45Kx/SFA/8S0AWRtbU1EN27c6HrY9evXicjGxobhdAAAA9o093FeU11JuVZifErek+dsJwKAnkD9AwDQFofDWRHgoz1Mi4hyCopvZj1kOxEAdIJpA8jX15eIDh8+fPny5ReNSUtLCw8PJ6K5c+cynA4AYKAL8Jnu4jiSiGQy+W8x5yuqa9lOBADdhvoHAKAdAz3dxb4zlMdnLlytrBGymwcAOmLaANq2bRuXy5VKpUuXLt29e3djY2PbT0Ui0d69e4OCgiQSCZfLffPNNxlOBwAw0HE4nJWBPnbW5kQkamr+JTK+oVHMdigA6B7UPwAAHU12cXQd60BELRJp1Nlk+cvekwWAPsa0AeTs7PzFF18QUU1Nzfbt2y0tLadOnRoSEhISEjJ16lQLC4u33367qqqKiD7//HNnZ2c1RAYAGOA0NTQ2LF9oYmRARNXC+l+PJ7RIpGyHAoBuQP0DANCppQu99HR1iOjJs7LUG1lsxwGA/0OD+S127NghEAjeeuut+vr6+vr6jIyMjIyMtgP09PS+/vrrP/3pT8znAgAYHHS1h21c4b/vcEyjqKm4tCLiVNK65Qu5WC8WYOBA/QMA0JGu9rBlfrPCTyQS0fkrfziOsrM0NWY7FAD8B9MngJQ2btxYXFy8b9++kJAQJycnc3Nzc3NzJyenkJCQ7777rqioKCwsDBthAAC0JTDUfyV4gQaPR0QP8p7GX3rJarIA0N+g/gEA6Gjc6BFT3MYSkVQmO3b6glQqYzsRAPyHGp4AUtLX19+yZcuWLVvUdUMAgEFvpI3FisU+EaeSFESpN+8YGQyfOdmF7VAA0A2ofwAAOlriO7OgqKSqpq6ssiYp7Q+/OdPYTgQARGpsACnJZLLi4uL8/Pza2lpjY2NnZ2dTU1P1TgEAMJi4OTlU1kw9d/kmEcVeuGagP3z8GHu2QwFA96D+AQBoi8/XXLHIe//RWLlCkXIja6yD7UhbS7ZDAYCaXgGTyWS//vrr9OnTtbW17e3t586du2zZMm9vbzMzswkTJuzfv18qxfqmAACdmztj4vSJzkSkUCiOnb749Hk524kAQCWofwAAXmSEjYXnVFciUigUUWeTm1skbCcCAHU0gCorK+fMmbNx48YbN25IJO3/Yt+5c+f111/39PQsL8ePNAAAnVsyb6bjSBsikkilh6PP1dQ1sJ0IAF4C9Q8AQNcWzp5qYWpMRNXC+rhL19mOAwCMG0BSqXTRokVpaWlExOPxVq9evXfv3tOnT8fHx3/77bfr16/X0NAgovT09ICAAPweDACgU1wud+3S+ZZmAiKqbxT9EhknbmpmOxQAvBDqHwCAl9Lg8VYs9uHxuER0I/PBw7ynbCcCGOqYrgH0008/3bx5k4imTp169OjR0aNHt/1069atf//731evXp2enp6RkfHzzz9v2rSJ4YwAAIOSFl9zY6j/d4djhHUN5VW1h6PPvbZykXKPMADob/qg/qmsrDx69GheXl5JSYlAIHBwcAgNDbW3t1flWqFQGBERkZ6eXltbKxAIZsyYsWrVKh0dnZ4N61pTU1NYWJinpydWwgaAjqzMBL6ek5VrHR6PS3n7TyHDdbTZDgUwdDF9Aui3334jIgsLi1OnTrWrfpQcHBxOnjxpbm7eOhgAADqlP1zntRC/YVp8IsovKomKS1GwHQkAOtXb9c/t27e3bt2alJRUUFCgpaX1/Pnz1NTUd9555+zZsy+9tqqq6p133omLi6usrNTQ0CgrK4uJidmxY0dDQ0MPhmVlZX3wwQcrV67cunVrREREx5fdYmNjxWLxihUruvsdAWCI8JnuPtLGgogaROKTialsxwEY0pg2gO7evUtEK1assLR84bruVlZWoaGhRHTv3j2G0wEADG7mpsZrlvhyuVwiyrqfe+HKH2wnAoBO9Gr9I5FIvv/+e7FYPHPmzCNHjhw+fDgiImLZsmUymezHH38sLCzs+vIDBw5UVlY6ODh89913v//+++7du83MzIqLiw8dOtTdYcnJyR9//HFeXt7o0aObm5sjIiI+++wzheJ/renGxsbo6OhFixYJBIJufUcAGDo4HE5ogDefr0lE9x4XZt7PZTsRwNDFtAEkEomIaOLEiV0PmzRpEhG1+50SAAB05OhgG7zQS3l8Ie2PP+4+ZjcPAHTUq/VPSkpKaWmpiYnJu+++q6+vT0Q6Ojqvvvqqt7e3TCaLiorq4lqhUJiens7n8z/44ANbW1sOhzNq1KgPP/yQiC5fvtzS0qL6sJaWlv3791tYWPzwww//7//9v/379/v7+2dmZl69erV1upiYGKlUGhIS0q0vCABDjbGhvr+3h/I45tyVWmx2AcASpg0gOzs7IiooKOh62NOnT4nI1taW4XQAAEPBVDenOdPdiUhBFB1/OaewmO1EAPB/9Gr9k5+fT0S+vr6ampptz/v6+rZ++iKpqakymczNzU359pmSg4ODg4ODSCTKyMhQfVhhYWFjY2NwcLCxsTER8Xi8DRs20H+ffiKiurq6U6dOBQYGGhgYdOsLAsAQNH2i81gHWyJqam45Ho+X3AHYwbQBtGzZMiKKiYlpbn7hhjVSqVT5ynpAQADD6QAAhgi/OdMmjh9DRDK5/EhMUklFNduJAOB/erX+efbsGf23x9SWoaEhEVVUVHRxbXZ2NnX2aJLyWaSsrKxuDeuIw+HIZDLlcXR0NJfLDQ4O7vrrAAAQEYdouf8cnWFaRJRb+OzaLawNAsACpg2gd99918rK6s6dO6+++mpTU1PHAVKpdPv27Tdv3jQyMvrrX//KcDoAgCGCQxTiP2eUnRURNTe3/BIVL6xvZDsUAPxHr9Y/Gzdu3LVr1+TJk9udz8nJIaK2z+x0VFNTQ0RWVlbtzivXKqqtrVV9mL29vY6OzsmTJ6uqqohIJpOFh4crFApXV1flHc6cORMUFKSnp9etbwcAQ5b+cJ2g+Z7K4/jkGxXVtezmARiCmG4Db2pqevbs2aVLlx47diw5OXnbtm3Tp08fOXKklpZWQUHBnTt3vv7665ycHF1d3d9//3348OEdX4MfPnw4wwwAAIMSj8ddu3Te90dOV9EZk5EAACAASURBVFTX1tU3HjqRuHlNoHINRQBgV6/WP53u9d7Q0BAZGUlEnp6eXQQTCoVEpKur2+l0yk9VHMbn819//fWvv/56y5YtY8aMKSsrKy8vnzRpkpeXFxFFRUXx+fygoKAuwgAAtDPBefS93Cd3HuRJJNJjsRffWLeUx2X6RAIAqI5pA8jJyYmIlIsFlpaWKpcP7KixsXHBggWdftR2LwkAAGhLR3vYxlC/fYdPNYjEz8oqfzt9YcOyBVyUSgBs6+P65/nz519++WVpaamxsfHixYu7GKns3XTsLnXaAHrpMG9vb0NDw2PHjuXm5goEgtWrV4eGhnI4nIqKioSEhDVr1ujo6Kj4FRoaGm7fvt3xvFQqbWhoeP78uYr3AYCBbsaEMflPnzc0ip+VVsaeT53mOobtRAADVQ96KUwbQI8ePWJ4BwAA6IKxof6rIX77I2IlEumjvKcx59OWLZzFdiiAoa7P6h+JRHLy5MnIyMiWlhY9Pb1PPvlEuS/Yi3RdC0okkm4NIyJ3d3d3d/d2AyIjI3V1dbtuRbXD4/E6XStaLBbzeDw+n6/6rQBgQOPz+X6zJp1ISFMQpd/JGW1nZWFqxHYogAGJhQbQwYMHGd4BAAC6ZmNpuiLA++ipCwqFIj3zgamxwaypbmyHAhjS+qb+efLkya5du5Rbibm6ur7zzjsmJiZdX2JoaCgWixsb2y8ZpnwHzcjIqFvDOlVaWnr+/PnXXntt2LBhRCQSiQ4fPpyRkSESiRwdHdevXz9y5MiOV2lra7u5dfIPV3l5uba29ku/FwAMJiYmJkVlNTdu35fL5efSbr/56jJNDaY/lgIMQXK5vLuXMP2bFhYWxvAOAADwUq5jHfy9G+IuXSeiuEs3jPT1XMZ28iMWAPSNPqh/EhMTDxw4IJFIjIyMNm7cOGfOHA6H89KrDA0NS0pKOnZ2lGfaNoBUGdapiIgIQ0NDPz8/Impubn7vvfeKiopsbW2NjY0zMzMzMzM///xz5StyAAAvsthnet6TZ5XVwvKq2nOXMwLmTmc7EcCQgIUkAAAGhtnT3GZOGk9ECoXi2JmLT56VsZ0IAHpLSkrKd999J5FIZsyY8f3333t7e6vS/aH/bhVfUlLS7nxZWVnrp6oP66ioqCg5OXnlypXKl7ZOnjxZVFQUFhb27bfffvHFF7t37+bz+T/88IOKXxMAhixNTY0Vi7y5HA4RXcnIznuKhcAA+oLaGkBCoXDfvn2hoaFOTk7m5uYCgWDMmDFLly7997//3brnKAAAMLF43kznMfZEJJXKDp1IrKwRsp0IYKjrjfqnpKTkm2++IaKVK1f+9a9/VX2hZSJS7tGelZXV7nxmZiYRubi4dGtYR0ePHjU1NZ0/f77yj9nZ2SYmJoGBgcr+lL29/bx58/Lz80UikeqZAWBosrM2n+MxgYgUCkXkmUvipma2EwEMfmpoACkUigMHDtjY2GzduvX48eOPHj0qLy+vrq7Ozc09derUtm3bbG1tDx48iN2+AAAY4nI4qwPn2lqaEVGjuOmXyPhGURPboQCGqN6rfxITEyUSia+v79q1a1V88KfVrFmzeDxeVlZW605eRFRaWvr48WMdHZ3p06d3a1g7+fn5aWlpq1at0mizWkenCxD0YFUCABiC5s2aYmNhSkTC+sazF6+zHQdg8FNDA2jnzp2bN29Wrhqor68/ZcqUkJCQVatWeXh4KLd7aGho2LRp09dff818LgCAIU5TU2P9sgVG+sOJqKq27sjJc1KpjO1QAENR79U/V65cISIPD4+azrRt2URFRUVERNy7d6/1jIGBwbRp08Ri8VdffaV8DEcoFO7cuVOhUPj4+LRutqXisHaOHj1qZWXl4+PTesbV1bW6ujo6OlrZ58rPz09KSnJwcOi4wTwAQEc8LndFgLeGBo+IMrIfZT/KZzsRwCDHdBHo7Ozsjz76iIgEAsEnn3wSFhbW9kFlsVh88ODBf/zjH1VVVe+///7ixYvHjh3LcEYAgCFOb7jOxlD/74+cEje3FBSXRp69tHqJb3cfEwAAJnqv/pFIJOXl5UT0r3/9q9MBZmZmP/74o/I4KiqqqamJy+WOHz++dcCmTZseP36clZW1du1aa2vroqIihUJhZ2e3bt26tvdRcVirx48fp6en79ixg8fjtZ4MDg5OTU0NDw9PTEzU09PLy8vjcDhvvPGGil8WAMDMxGiB15S45BtEFJN4ZaSN5XBdbbZDAQxaTJ8A+vbbb+Vyuaam5unTp7dt29buNXVtbe1t27bFxMRoaGjIZDLlC+0AAMCQmYnRumULeDwuEd15mH8uNYPtRABDS+/VPxUVFQyzCQSCPXv2+Pv7GxoaPnv2zNTUdNmyZbt27WoXUsVhrY4cOWJnZ+fl5dX2pJaW1q5duwICAhQKRUlJycSJE/fs2ePo6MjwKwDAkDJrmpuDrSURNYqbohMusx0HYDDjMFyax9HRMScn57XXXvvpp5+6GBYWFvbTTz85Ojo+evSIyXSDWFJSko2NDbZNBQDVZd7P/T32ovIf8aAFXjMmOrMciG0HI84otxH529ZX9IZ3Y91cGOjkcnlcXBwRubm52dnZ9cGMqH/UBfUPABBRtbB+7y8nmptbiCh0kfdkV/SRAV6uB/UP0yeAnj17RkQeHh5dD1OuJlhcXMxwOgAAaOXuPHqu5yTlcez5tAe5T9jNAzB0oP4BAFAjYwO9xT7/WX7+9IWrNXUN7OYBGKyYNoCUDxC99DEiLE4BANAb5nlNmeTiSERyheJY7MXn5VVsJwIYElD/AACo19QJTi6OI4moubnl99iLcmwhDdALmDaArK2tiejGjRtdD7t+/ToR2djYMJwOAADa4hAt9589eoQ1ETW3SH6Nihfil2YAvQ/1DwCA2gUvnKVcAbqwuDQtI5vtOACDENMGkK+vLxEdPnz48uUXrteVlpYWHh5ORHPnzmU4HQAAtMPjctcFz7cwNSaiugbRz8cTmppb2A4FMMih/gEAUDtdnWHLFs5SHiem3CytqGY3D8Dgw7QBtG3bNi6XK5VKly5dunv37sbGxrafikSivXv3BgUFSSQSLvf/s3fncVWW+/7/P2stWMyTgAIqKg6ZU0qpqJjglGOG4lBmptttWqdOx62/s2vvHns/Tmefxke6d7W31smdWWCioCkOSYqSE1KK4ESCAyogIIPMrOH3x+qw/eIErAX3WovX86/be133ut63lX38cN/XpX711VfNnA4AcDcnJ+3CWU95uLmKSEHhrZjtSQaDQelQgD2j/gGA1tCvd/fQ/r1FRKfXb05M1uupZwBLMrcB1K9fv/fee09ESkpKVqxYERgYOHTo0Ojo6Ojo6KFDhwYEBLz++uvFxcUi8s477/Tr1953qAGAVuLj5fFi9CSto4OIZF26lrD3R6UTAfaM+gcAWsnTE0Z5e7qLyI2Cov1HflY6DmBXHMz/ipUrV/r6+v77v//77du3b9++nZaWlpaWducADw+Pjz766De/+Y35cwEA7qdzgN9zM8Z/tXWvwWg8cfq8r49nRNhgpUMBdov6BwBag7OTdvaUiP/dtNMocuDYqUd6BgcHdVQ6FGAnLNAAEpFFixbNmjXrm2++2b9/f2ZmZklJiYj4+PgMGDAgMjJy/vz5Xl5eFpkIAPAAfXsGz5gYnrA3RUT2Hkz18nAb0r+30qEAu0X9AwCtoWe3oJGPDzj8U6bBYPh2x/5/XzRLq3VUOhRgDyzTABIRT0/P5cuXL1++3FJfCABogeGDHy0sLv0xLcMosmX3QU8Pt57BQUqHAuwW9Q8AtIbJEcMvXrleUFRSXFq+52Dq0xNGKZ0IsAfmrgH0ySeffPLJJ/v27bNIGgCA+aaODRvQp4eI6PWGbxL2Fd4qVToRYG+ofwCgVTk4aOZMi9So1SJy9OczF3JylU4E2ANzG0Cvv/76q6++aloHEQBgDVQq1dzpkcGdO4lIVU3tPzfvrqisVjoUYFeofwCgtXXu5Bc5YoiIGEW27j5YVVOrdCLA5pnbAHr00UdF5OLFi5YIAwCwDEcHh4WznvL18RSRW2W3v9y6p75ep3QowH5Q/wBAGxg7ckjXwI4iUl5RtWPfYaXjADbP3AbQW2+9JSJXrlxJTEy0RB4AgGW4uTi/OGuSq7OTiFzLK4z57geD0ah0KMBOUP8AQBtQq9VzpkY4OjiIyMmzF0+fy1Y6EWDbzG0AzZkzZ82aNWq1eunSpQcPHrRIJgCARfj7ei+Y9ZSDRiMi5y5e2Z18XOlEgJ2g/gGAtuHv6z1pzDDTccLelLLblcrmAWyaubuA7dmz55FHHvnDH/7wP//zPxEREU888UT//v0DAwPd3NzuOf6Pf/yjmTMCAJquR5eAOVMjYr/7wSiSknrax9N95OMDlA4F2DzqHwBoMyOfGHAh52rWpWvVtXVbdh9cPGeKSulIgI0ytwE0efLkO3+ZlpaWlpb2gPEUQADQxgY92rOopOz7lDQR2fnDUW9P9369uysdCrBt1D8A0GZUItGTx6xev6W6pvaXS9dST50bPvhRpUMBNsncBtD9ftIFALAeY0eGllVUHT951mA0xu7Yv3TetK5BHZUOBdgw6h8AaEueHm7Tx43YnJgsIon7j/bsFuTn46V0KMD2mNsAqqiosEgOAECrenr8yFul5b9culZfr/sq4fuXFzzj4+mudCjAVlH/AEAbCx3Q51z21YzzOXX1urjE5JfmP61W8SoY0DzmLgINALAJGrX6+WcmBHb0FZHbFVX/jNtdXVOrdCgAAICmemZiuIebq4hcuV5w6Hi60nEA22PhBpBer79y5cqBAwcSEhIOHjxYWFho2e8HALSYk9Zx0ezJXp7uInKzqGRj/Pc6vV7pUIA9oP4BgDbg5uI8c9Jo0/G+H9PybhYrmwewOZZpAOn1+i+//DIsLMzFxaV79+5jx46dOXNmREREx44dH3vssXXr1ul0OotMBAAwh6e76+LoSc5OWhHJyc2L23XQqHQkwHZR/wBAG3u0V7ehg/qKiF5viN2xX6fjR1lAM1igAVRUVDRmzJhFixYdP368vr6+0aenT59etmzZqFGjbt68af5cAAAzdfLv8OzT49RqtYikn724//DPSicCbBL1DwAoYvr4kb4+niJys6hk3+GflI4D2BJzF4HW6XRTpkw5ceKEiGg0mjlz5oSFhfXo0cPR0TE7Ozs1NTUmJkan06Wmpk6dOvXo0aMODubOCAAw0yMhXaMmhm/dc0hEkn5M8/Zyf3xAH6VDAbaE+gcAlKJ1dJgzJWJdzA6D0XjoePojIV1DugYqHQqwDeY+AfTFF1+Yqp+hQ4eeP38+Jibmtddemz59+qRJk1555ZUNGzZcuHBh2LBhIpKWlrZ+/XoLRAYAmG3oY33HDH9MRIwi8bsPXbx8XelEgC2h/gEABXXrEhA+dKCIGI3GLYnJtXWNH8MEcE/mNoC++eYbEQkICNi+fXuvXr3uHhASEpKQkNCpU6eGwQAAazApYviQfr1ERG8wbNy2L7/wltKJAJtB/QMAypr45NBA/w4icqvsduKBY0rHAWyDuQ2gzMxMEZkzZ05g4H2fuwsKCpo9e7aInDlzxszpAACWohKZNXlMty4BIlJbW7dh697blVVKhwJsA/UPACjLQaOZPS1So1GLSOqpc+eyryqdCLAB5jaAqqqqRGTIkCEPHhYaGioiFRUVZk4HALAgBwfNwpkT/Tp4iUhJ2e0vt+ypq2fTIuDhqH8AQHFBHX3Hj3rcdLx118GKqmpl8wDWz9wGUHBwsIhcunTpwcOuXr0qIl27djVzOgCAZbm6OC+ePdnd1UVErucXxWxPMhjZGh54COofALAGEWGDe3QJEJGKquqEPSlKxwGsnbkNoJkzZ4rItm3bamtr7zdGp9MlJiaKyNSpU82ZKykp6emnn87Ly2v6JWVlZWvXrl28ePHMmTN/+9vfrl+/3vQju5YNe7Camprnn3/+H//4R3MvBABldfD2XBj9lKOjg4icz766/fsflU4EWLu2rH8AAPejUqlmT41w0jqKyJlfLp88e1HpRIBVM7cB9Lvf/S4oKOj06dMvvvhiTU3N3QN0Ot2KFStOnDjh4+Pz+9//vsUTGY3GgwcPNuuS4uLi//iP/9i1a1dRUZGDg0NBQcG2bdtWrlzZ6EnsJg5LT09/44035s6d+8orr8TGxtbXN15qfseOHdXV1XPmzGnZDQKAgroGdpwzNUKlUonI8VPnfjyRoXQiwKq1Wf0DAHiwDt6ekyOGm463f/9jaTlv3QL3ZW4DyN/fPzExsVu3bps2berRo8c777xz4MCBy5cv5+XlHTlyZO3atf369fv444/d3Ny+/fZbd3f3irs0ZRaDwbB9+/b09PRmZfvss8+KiopCQkI+/fTTb7/9dvXq1R07drx27dpXX33V3GHJyclvvfVWdnZ2r169amtrY2Nj3377beMdb0lUVlbGx8dPmTLF19e3WSEBwEoMfCRk0phhpuPEA8cysx7ybgvQnrVN/QMAaIrhQ/o90jNYRGpq67bsOsir7MD9OJh5fd++fUWkrq5ORPLz89988817DqusrJw4ceI9PzI+cLGJtLS0H3/8MTMz8+bNm80KVlZWlpqaqtVq33jjDdMmrD179nzzzTdff/31Q4cOLVmyRKvVNnFYXV3dunXrAgIC3n333Q4dOuj1+s8++2z37t1HjhwZNWqUabpt27bpdLro6OhmhQQAqzJm+GNl5RVHfj5jNBq/3XHAc55rcOdOSocCrFFr1z8AgKZTicya9OSa9VuqqmsuXrl+9KfMkY8PUDoUYI3MfQLowoULFy5caNa6PM1y9OjR/fv3N7f7IyIpKSl6vX7QoEGmto5JSEhISEhIVVVVWlpa04ddvny5srIyKiqqQ4cOIqLRaBYuXCj/twWsiJSXl2/fvn369OleXl5m3CsAKG/a+JH9encXkXqdbsPWvUUlZUonAqxRa9c/AIBm8XR3nTlptOl4V/LxgqISZfMA1sncJ4A+//xzi+S4n3nz5jUsnfjmm282fW3mjIwMudf+rKGhoTk5Oenp6SNHjmz6sLupVCq9Xm86jo+PV6vVUVFRTcwGAFZLrVI9O33sZ7E7c/NuVlbX/HPz7pcXPOPm6qx0LsC6tHb9AwBorgF9ejz2aM/0c9k6nX5z4oGXFzyjUZv7uANgZ8xtAC1ZssQiOe7H39/f39/fdKzRaJp+YUlJiYgEBQU1Oh8YGCgipaWlTR/WvXt3V1fXhISEYcOG+fr66vX6DRs2GI3GgQMHmr5h586ds2bN8vDwaP79AYDVcXR0eGHmxE83bistryguLf864fvfzJvq0Jw/gQG719r1DwCgBZ55avTl6wVl5RXX84uSj54aNypU6USAdbHbnmhZWZmIuLm5NTrv7u7e8GkTh2m12mXLluXn5y9fvvwPf/jDSy+9tHv37tDQ0PDwcBGJi4vTarUzZsxo3fsBgDbk4e66ePZkFyetiFy6lr85MZnVSgAAgJVzcdJGT35SJSIiPxz5OTev2QuJAPbN3CeArJapd2Pq49zpng2ghw6LiIjw9vbetGnTxYsXfX19n3322dmzZ6tUqsLCwj179jz33HOurq5NDFZZWWl676wRvV5fWVmZn5/fxO8BgNY2NeKJ+H1HDQbj6XPZzg7qkaGPKp3oIUwr8orIzZs3Kyt4ba0dYUFlAIBJ7+5dhg/pd+zkWYPBsDkx+bUXZzo62O3feYHmstv/GB5cC9bX1zdrmIgMHjx48ODBjQZs3rzZzc1t2rRpLY0JANarS4DfuBGP7Tt8SkRSM35xd3MZ9Eh3pUMBAAA8yNTIsItXrhfdKissLt176MS0sSOUTgRYC7ttAHl7e1dXV1dWVjY6X1FRISI+Pj7NGnZP+fn5+/btW7x4sbOzs4hUVVVt3LgxLS2tqqqqT58+L7zwQo8ePe6+ys3NLSws7O7zSUlJbm5uAQEBTb1DAGh9AQEBeqN6/5GfReRgamb34C59enRROtR9abVa00HHjh093Jv6YCbsgMFgUDoCAMBaODo6zJkSsfab7wxG4+G0zEd7duvZrfGSr0D7ZLdrAHl7e4vI3Z0d05k7G0BNGXZPsbGx3t7ekyZNEpHa2tpVq1YlJiZqtdouXbqcOnVqxYoV58+ft8zNAIByJox+InRAHxHRGwzfbNuXd7NY6UQAAAAPEty505iwwSJiNBo3Jx6orqlVOhFgFey8AZSXl9fofEFBQcOnTR92t9zc3OTk5Llz55p+4JyQkJCbm7tkyZJPPvnkvffeW716tVarXbt2rcXuBwAUohKZNfnJXt06i0htXf0/43aXlVcoHQoAAOBBJoQ/3iXAX0TKblfu3H9U6TiAVbDbBpBpj/b09PRG50+dOiUiAwYMaNawu8XExPj7+0+YMMH0y4yMDD8/v+nTp6tUKhHp3r37+PHjc3JyqqqqLHI7AKAgjVr9fNSETv4dRKS8omr9lj01tXVKhwIAALgvtVo9Z2qEaQXonzKyMi7kKJ0IUJ7dNoBGjx6t0WjS09MbdvISkfz8/KysLFdX14ZVeJo4rJGcnJzDhw/PmzfP4Y4l5e+5AAGrEgCwD85O2hdnPeXu5iIiBYW3Yr77gT/fAACANevo5zNh9BOm4217f7xdyc/m0d7ZSQMoLi4uNjb2zJkzDWe8vLyGDRtWXV394Ycfmh7DKSsre/fdd41GY2RkZMNCoU0c1khMTExQUFBkZGTDmYEDB966dSs+Pt60rVhOTk5SUlJISMjdG8wDgI3y8fJYFD1Z6+ggIlk5uQl7f1Q6EQAAwIOMHjowJDhIRCqraxL2pCgdB1CYZXYB0+l0O3fuPHToUO/evZcvX95wftmyZTdu3JgwYcJLL710v2aKRcTFxdXU1KjV6v79+zecXLp0aVZWVnp6+vz58zt37pybm2s0GoODgxcsWHDntU0c1iArKys1NXXlypUajabhZFRUVEpKyoYNG/bu3evh4ZGdna1SqV5++eVWul8AUETnAL/nZoz/auteg9F44vR5Px9P0wqLQPukeP0DAHgwlUo1e8qYNf/cWltbd/bilbTTF54Y9IjSoQDFWOAJoKtXr0ZGRkZFRa1evTo7O/vOj0pLS3fs2PHaa6+NHDnyl19+MX+uZvH19V2zZs3kyZO9vb2vX7/u7+8/c+bMDz74wNXVtQXDGnz99dfBwcHh4eF3nnRycvrggw+mTp1qNBrz8vKGDBmyZs2aPn36tOLtAYAS+vYMnjL219dj9xxMPXmmrf9sB6yE1dY/AIA7+Xh5TPu/0uW7H44Ul5YrmwdQkLlPANXV1Y0bN+7ixYsi4unp2atXrzs/feqppzIyMs6ePfvTTz/NnDnz5MmTdy6a01zffPPN/T7avHnzPc97eXktX778zh/KmTPM5L/+67/ued7V1fWll1566aWXmvIlAGC7wp8YWFJ2+3BaplFky+6Dnh5uPYODlA4FtKm2rH8AAGYaOqjvhezczKxLdXX1cYnJS5+brlaplA4FKMDcJ4D+8Y9/mKqfFStWFBYWLlu27M5PFy1alJmZ+fe//11EMjMz161bZ+Z0AABrMG3siP59uouIXm/4ZltS4a1SpRMBbYr6BwBsS9RTo017WVy+ln/4RIbScQBlmNsASkhIEJGxY8d++OGH93zLXaVSLV++fO7cuSKyc+dOM6cDAFgDlUo1b/rY4KCOIlJVXfPPuD0VVdVKhwLaDvUPANgWN1fnmU+NNh3vPXQiv/CWsnkARZjbADK92T5r1izVAx+imzhxooicO3fOzOkAAFbC0cFhYfQkXx9PEblVWv7llj319TqlQwFthPoHAGxOv97dQwf0ERGdXr85MVmvNyidCGhr5jaAioqKRMTT0/PBw1xcXEQkPz/fzOkAANbDzcX5xVmTXJydRORaXuHmxGSj0ah0KKAtUP8AgC16evxIH093EblRUPTDkZ+VjgO0NXMbQIGBgSLy888P+Y/np59+EpFOnTqZOR0AwKr4+3q/MHOig0YjIhkXcnYfTFU6EdAWqH8AwBY5O2nnTB9renjzwNGTl67RoEf7Ym4DaOzYsSLy9ddfX7p06X5jcnNzv/rqKxEZPXq0mdMBAKxNj66Bs6dGmF6DOXQ8/cjPZxQOBLQ+6h8AsFE9ugSMfLy/iBiNxrjE5Lq6eqUTAW3H3AbQq6++qlKpCgsLJ0+evG/fvrsf/j98+PC0adMKCwtFhC3SAcAuPfZoz/GjnzAd70w6cvaXy4rGAVod9Q8A2K7JEcM7+XcQkVul5Ty8jHbFwczrhwwZ8uc///lPf/rThQsXJk6c2K9fv8GDBwcHBzs5OV29ejUzM/PEiROmkS+//DI/AQMAezVuZGh5RdXxk2cNRmPsjv1Ln53WNbCj0qGA1kL9AwC2y0GjmTM14u9fbdMbDMd+PtO3Z/AjIV2VDgW0BXMbQCLy1ltveXp6/v73v6+trT179uzZs2cbDVCpVCtXrnznnXfMnwsAYLWeHj/yVkn5L5ev1dfrvor//uUFz5jWWQTsEvUPANiuzp38IkcOSfrxJ6PI1t0HX//NbFdnJ6VDAa3O3FfARESlUr3++uvZ2dl/+tOfhg8frtVqG8737dv33/7t3zIyMt5//32NRmP+XAAAq6VRq59/ZnxgR18RuV1R9c+43dW1dUqHAloL9Q8A2LSxI4Z0DeooIuUVVdv3HVY6DtAWLNAAMuncufOf//znY8eO1dTUlJWVFRcX19fXnzt37uOPP+7fv7+lZgEAWDMnJ+2i6EleHm4icrOoZGP893q9QelQQCui/gEAG6VWq+dMiXB0dBCR9LMXT5/LVjoR0Oos1gBqoFKpPD09O3TowI+8AKAd8vRwWzjrKa3WUURyrt5I2JuidCKgLVD/AIDN8ff1njRmmOk4YW9K2e1KZfMArc0CawABAHCnoE5+82eM37B1r8Fg5xZmbgAAIABJREFUSMu44OPtMW5kqNKhAAAAGhv5+IALOblZObnVtXVbdh1cPHeKSulId8u+eqP4VplZX6FSDXusr4XiwIY1uwE0ePBg00FSUpKfn99///d/N+vyP/7xj82dEQBgcx4J6frMxPD4PYdEJCklzcfTPXRAH6VDAS1H/QMAdkklEj3pydXrt1TX1P5y+VrqybPDh/RTOlRjaacvnDzziznfoKYBBBFpQQMoPT3ddKDT6UTkrbfeatblFEAA0E4Me6xvUUnZoePpRpGtuw95erj16tZZ6VBAC1H/AIC98vRwmz5+5OadB0Qk8cCxnt07+/l4KR0KaBXmvgLm5uZmkRwAAPszOWJ4+e3KU2cv6g2GjQn7ls9/OsC/g9KhAAug/gEAexLav/f5i1dOn8+pq9dtTkxeNv9ptcqKXgUb9GjPAD+f+32amn6+uLRcRMaGDXFycrz3IGu6HSio2Q2glJRfl/Ps0KGDiFRUVFg4EQDAXqhEoiePKSmvuHItv7a2bsPWvS8vmOHh5qp0LqDZqH8AwL7NmBh+KTf/dmXV1esFB4+nR4YNVjrRvzzaM/jRnsH3+zTr0jVTA2jE4/093Kmy8CDN3gUs/P9otdrWCAQAsCcODpqFMyf6dfASkZKy219u2VNXr1M6FNBs1D8AYN/cXJxnT40wPSeTlJJ2Lb9Q4UBAK7DwNvB6vf7KlSsHDhxISEg4ePBgYSH/2QBAe+fq4rx49mR3VxcRuZ5fFLM9yWA0Kh0KsCTqHwCwA316dHnisb4iojcYNicm63R6pRMBFmaZBpBer//yyy/DwsJcXFy6d+8+duzYmTNnRkREdOzY8bHHHlu3bp1pxUQAQPvUwdtzYfRTjo4OInI+++r2739UOhFgAdQ/AGBnpo8b6evjKSI3i0r2/ZimdBzAwizQACoqKhozZsyiRYuOHz9eX1/f6NPTp08vW7Zs1KhRN2/eNH8uAICN6hrYcc6UCJVKJSLHT507nJapdCLALNQ/AGB/tI4Oc6ZEmFaAPpR6Oic3T+lEgCWZuwuYTqebMmXKiRMnRESj0cyZMycsLKxHjx6Ojo7Z2dmpqakxMTE6nS41NXXq1KlHjx51cDB3RgCAjRrYN2RSafnug6kisnP/US9PtwF9eigdCmgJ6h8AsFfdugSEDxt06Hi60Wjckpj874ujnbT32VoLsDXmliNffPGFqfoZOnRoTExMr1697vz0lVde+dOf/vTss8+mpqampaWtX79+6dKlZs4IALBdY8IGl96uPPrzGaPR+O2OA57zXIM7d1I6FNBs1D8AYMcmjn7il5zcvMJbt8puJ+4/OnPSk0onAizD3FfAvvnmGxEJCAjYvn17o+rHJCQkJCEhoVOnTg2DAQDt2fTxI/v16iYi9Trdhq17i0vKlU4ENBv1DwDYMQeNZt7T4xwcNCKSmn7+XPZVpRMBlmFuAygzM1NE5syZExgYeL8xQUFBs2fPFpEzZ86YOR0AwNapVap508cGdfITkcrqmi+37qmqqVU6FNA81D8AYN86+fmMG/W46XjLruSKqmpl8wAWYW4DqKqqSkSGDBny4GGhoaEiUlFRYeZ0AAA7oNU6Loqe5O3pLiKFxaUbt+7V6dlpFbaE+gcA7F7E8Md6dA0UkcqqmoQ9KUrHASzA3AZQcHCwiFy6dOnBw65evSoiXbt2NXM6AIB98HB3XTR7souTVkQuXcvfnJhsVDoS0HTUPwBg91Qq1ewpY0wrQJ/55fLPZ35ROhFgLnMbQDNnzhSRbdu21dbe9wF+nU6XmJgoIlOnTjVzOgCA3ejk5/P8zIkajVpETp/L3peSpnQioKmofwCgPejg7TklYrjp+Lt9h0vLeaITts3cBtDvfve7oKCg06dPv/jiizU1NXcP0Ol0K1asOHHihI+Pz+9//3szpwMA2JOewUFRE0ebjvcf+fn4ybPK5gGaiPoHANqJYUP69e0ZLCI1tXVbdh3kgWXYNHMbQP7+/omJid26ddu0aVOPHj3eeeedAwcOXL58OS8v78iRI2vXru3Xr9/HH3/s5ub27bffuru7V9zFIrcBALBRTwx6ZOyIXxdS+S7pyC+XrimbB2gK6h8AaCdUIrMmPenq4iwiF69cP/JTptKJgJZzMPP6vn37ikhdXZ2I5Ofnv/nmm/ccVllZOXHixHt+ZDTSRQWAdm3Ck0NLyyt+PvOL3mD4etu+ZfOfDuzoq3Qo4EGofwCg/fBwd5016cmNCd+LyO7k4726de7k56N0KKAlzH0C6MKFCxcuXMjLy7NIGgBAO6QSmTVlTK9unUWktq7+n3G7y3jHHtaN+gcA2pX+fbo/1q+XiOh0+s07D+gNBqUTAS1h7hNAn3/+uUVyAADaM41a/XzUhH98811B4a3yiqr1W/Ysn/+0s5NW6VzAvbVl/ZOUlPS3v/1t3bp1gYGBTbykrKwsNjY2NTW1tLTU19d3xIgR8+bNc3V1bdmwB6upqVmyZMmoUaOWL1/erAsBwLY8MzH88rX8svKK6wVFB46eHD/qcaUTAc1mbgNoyZIlFskBAGjnnJ20C2c99feN2yoqqwsKb8V+98PCWU+p1eY+qQq0hjarf4xG48GDB5t1SXFx8apVq4qKikTE2dm5oKBg27ZtaWlp77//vru7e3OHpaenb9q0KScnx8/PLzw8PDo62tHR8c7pduzYUV1dPWfOHLPuEwCsnouTdvbkMV98m2gU2X/k5CMhXbsGdlQ6FNA8FNYAAGvRwcvjxehJWkcHEbmQk5vw/Y9KJwKUZDAYtm/fnp6e3qyrPvvss6KiopCQkE8//fTbb79dvXp1x44dr1279tVXXzV3WHJy8ltvvZWdnd2rV6/a2trY2Ni33377ztWLKisr4+Pjp0yZ4uvLul0A7F+v7p3DhvQTEYPBsDkxuV6nUzoR0DwWawCVlZX9/e9/nz17dt++fTt16uTr69u7d+9nnnnm448/Li0ttdQsAAD71iXA/9kZ49UqlYicSD9/8Hjz/uoLtLFWqn/S0tLWrFmzdOnS9evXNzdPamqqVqt94403unbtqlKpevbsaVqj+tChQ6ZVq5s4rK6ubt26dQEBAWvXrv3LX/6ybt26yZMnnzp16siRIw3Tbdu2TafTRUdHt/hOAcC2TIkM8+vgJSKFxaV7D55QOg7QPBZoABmNxs8++6xLly6vvPLKli1bLly4cPPmzVu3bl28eHH79u2vvfZa165dP//8c3a7AAA0xaM9g6dEhpmO9yQfP3n2orJ5gHtq1frn6NGj+/fvv3nzZnMvTElJ0ev1gwYN6tSpU8PJkJCQkJCQqqqqtLS0pg+7fPlyZWVlVFRUhw4dRESj0SxcuFBEMjN/3QK5vLx8+/bt06dP9/LyasE9AoAtcnR0eHb6OI1aLSKH0zKyLl1TOhHQDBZoAL377rsvvfRSRUWFiHh6ej7xxBPR0dHz5s0bPny4qSCoqKhYunTpRx99ZP5cAID2IHzowFGPDxARo8jW3QevXMtXOhHQWKvWP/PmzVvzf5q1KnNGRoaIDBkypNH50NBQEWl4m6yJw+6mUqn0er3pOD4+Xq1WR0VFNT0eANiBzgF+Y8IGi4hRZMvug9U1tUonAprK3AZQRkbGH//4RxHx9fX961//mpeXd+LEibi4uNjY2GPHjuXl5f31r381vRb+n//5nxcuXLBAZABAOzBt3Ij+fbqLiE6n3xD/fdGtMqUTAf/S2vWPv79/yP/RaDRNv7CkpEREgoKCGp037SDW8FZaU4Z1797d1dU1ISGhuLhYRPR6/YYNG4xG48CBA03fsHPnzhkzZnh4eDT37gDA1o0fFdol0F9Eym9X7vjhqNJxgKYydxewTz75xGAwODo6fvfddyNHjmz0qYuLy2uvvRYaGhoZGanT6f72t799+umnZs4IAGgPVCrVvOljP4/defXGzarqmvVxu19eMMPd1UXpXICIFdc/ZWVlIuLm5tbovGljL9OnTRym1WqXLVv20UcfLV++vHfv3gUFBTdv3gwNDQ0PDxeRuLg4rVY7Y8aMJgbT6/Wmp6UaMRqNer2+tpafnwOwMc9MGLUuZme9TvdzZlav4EDTT60UYTAYTAd1dXW1tc34mQFsXcM/+qYztwF04MABEVmwYMHd1U+D8PDwhQsXfvHFF0lJSWZOBwBoPxwdHBbOmvT3r7cVl5TfKi3fsGXv0menOTqa+38uwHxWW/+Yejd37uNucs8G0EOHRUREeHt7b9q06eLFi76+vs8+++zs2bNVKlVhYeGePXuee+65pr+eVlFRkZKScvd5R0fH27dv37hxo4nfAwDW44n+IUfTs0Tku6QjTg5GV2cnRWI0LPCfn5/v6qJMBiiiBesMmltGX79+XUSGDx/+4GFhYWFffPHFtWsskQUAaAY3V+eFsyb94+vt1TW1uXk3N+9Kfu7pcSqVSulcaO+stv55cC1YX1/frGEiMnjw4MGDBzcasHnzZjc3t2nTpjU9mLu7++jRo+8+n5qa6uHhcffLaABg/QIDA/OKyy9fy6+pq0/NvPTc02MViaHVZpgOAgIC3N14VrodMRgMZ8+ebdYl5jaATAXEQztPFOsAgJbp6Ov9wsyJX3y7S6fXZ5zP2ePlMTniIX/rBlqb1dY/3t7e1dXVlZWVjc6bXr/y8fFp1rB7ys/P37dv3+LFi52dnUWkqqpq48aNaWlpVVVVffr0eeGFF3r06HH3VRqN5p6bhalUKo1G4+TEj6wB2KS50yLX/HNrbW3dhZzc0xcuDR3Ut+0zqNW/Luyr1Wr547RdacErYOYuAt25c2cROX78+IOHHTt2TES6dOli5nQAgHaoR9fA2VMjTH+TPng8/ejPZxQOhHbPausfb29vEbm7s2M6c2cDqCnD7ik2Ntbb23vSpEkiUltbu2rVqsTERK1W26VLl1OnTq1YseL8+fOWuRkAsHo+Xh7Tx44wHe9IOlJcUq5sHuDBzG0AjRs3TkQ2btx46NCh+405fPjwhg0bRGTsWGUeigMA2LrHHu05Lvxx0/GOpCNnf7msaBy0d1Zb/5g6O3l5eY3OFxQUNHza9GF3y83NTU5Onjt3rlarFZGEhITc3NwlS5Z88skn77333urVq7Va7dq1ay12PwBg9Z4Y9MiAR3qISF29bvOuZEPzl2UB2oy5DaDXXntNrVbrdLpnnnlm9erVjX6UVFVV9de//nXGjBn19fVqtfrVV181czoAQLs1btTjjw/sIyIGozF2x/7cvJtKJ0L7ZbX1j2mP9vT09EbnT506JSIDBgxo1rC7xcTE+Pv7T5gwwfTLjIwMPz+/6dOnm1526969+/jx43NycqqqqixyOwBgE6ImjjYtvnPlWv6PJzKUjgPcl7kNoH79+r333nsiUlJSsmLFisDAwKFDh0ZHR0dHRw8dOjQgIOD1118vLi4WkXfeeadfv34WiAwAaJdUIjMnPdm7excRqa/XfRX/fWn5PXaVBtqA1dY/o0eP1mg06enpDTt5iUh+fn5WVparq2tYWFizhjWSk5Nz+PDhefPmOTj8axHJey5A0IJVCQDAdrm5Os986td17r8/dCKv8JayeYD7MbcBJCIrV65cv369h4eHiNy+fTstLW3r1q1bt25NS0u7ffu2iHh4eHz++eerVq0yfy4AQHumUauff2Z8oH8HEbldUbU+bnd1bZ3SodBOWUP9ExcXFxsbe+bMv1bF8vLyGjZsWHV19Ycffmh6DKesrOzdd981Go2RkZGm97aaPqyRmJiYoKCgyMjIhjMDBw68detWfHy8aT3snJycpKSkkJCQuzeYBwD71q9398cH9BERnV4ft/OAXk8fHNbI3F3ATBYtWjRr1qxvvvlm//79mZmZJSUlIuLj4zNgwIDIyMj58+ffc98HAACay8lJu2j25E83biu7XXmzqGRj/Pe/mTNFo7HAzzOA5lK8/omLi6upqVGr1f379284uXTp0qysrPT09Pnz53fu3Dk3N9doNAYHBy9YsODOa5s4rEFWVlZqaurKlSs1Gk3DyaioqJSUlA0bNuzdu9fDwyM7O1ulUr388sutdL8AYM2eHj8y5+qNkvKKGzeLfzj808QnhyqdCGjMMg0gEfH09Fy+fPny5cst9YUAANyTp4fbC7OeWhezo66uPufqjYS9KdFTxigdCu2UFdY/vr6+a9asiYmJSU1NvX79ur+/f3h4+Ny5c11cXFowrMHXX38dHBwcHh5+50knJ6cPPvjAtA18Xl7ekCFDXnzxxW7durXi7QGAtXJy0s6ZPvazmB1Go/HAsVO9Q7r26BKgdCjg/6EyNmeV8v/93/8VkbCwsAesDoiWSUpK6tKlS9++fZUOAgC24UJO7oate01LjUSOGOLm6nzgyMnK6hoRCRv86IjHB3Tye9BW1rAnBoNh165dIjJo0KDg4GCLfz/1T+uh/gFgZ3b+cPTHtAwR6eDt+fqiWVqtY2vP+HnszuyrN0TkD6887+Hu2trTwXq0oP5p3jPzv/3tb3/7298mJSW1JB0AAJbzSEjXZyb++jBC8tGTO384aur+iMixU+fWfBGXuP8YW7HCIqh/AABNNCliWCf/DiJyq7R8d/JxpeMA/w8WTQAA2Kphj/Ud1DdERO5u8xhFUk6cTtx/tO1TAQCAdstBo5k7NcK0OuGxk2cvZF9VOhHwLzSAAAC2yijy4J1Wj/x05sbN4jbLAwAAENTJb+yIUBEximzdc6iqplbpRMCvaAABAGzVtRs3C4tLHzDAaDT+nJnVZnkAAABEJHLE4K5BHUWkvKIqfvchpeMAv6IBBACwVU15uudGAU8AAQCANqVWq+dNH2taAToz61L6uWylEwEiNIAAALartrbOImMAAAAsy9fbc9KTQ03H2/amlJVXKJsHEBGHFlyTmpr65Zdftmy+F198sWUXAgDQiLuby8PHsB8qLIT6BwDQLCMeH3AhJ/dCTm51bd2W3YcWz52iUjoS2rmWNIBiY2NjY2NbNh8FEADAUkKCg1T32gLsTj2Dg9ooDewd9Q8AoFlUIrMmPblm/ZaqmtpfLl87fvJs2JB+SodCu8YrYAAAW+Xt6d7/kR4PGODi7PT4wD5tlgcAAOBOnh5u08ePNB0nHjhWdKtM2Txo51ryBNCCBQumTJli8SgAADTXjAmj8gqKi0vL7/5IrVbPnhrh5uLc9qlgl6h/AAAtMKR/73MXr5w+n1Nfr9u8K3nZ/KfVKl4FgzJa0gAKDQ2dN2+exaMAANBcHm6uyxfM2L7vcOaFS0bjv94G6+jnEzUxvEfXQAWzwc5Q/wAAWuaZieGXruXfrqi6er3g4LFTkSOGKJ0I7RSvgAEAbJu7q8v8GeP/v5fmdfT1Np15cdZT/7E4mu4PAACwBq4uznOmRJge+0n68adr+YUKB0J7RQMIAGAPfLw8PNx+3fCrc4C/ioerAQCA1ejdo8vQx/qKiN5g2JyYrNPplU6E9ogGEAAAAAAArWvauJF+Pl4icrOo5Psf05SOg/aIBhAAAAAAAK1L6+gwe2qEaQXolNTTOVdvKJ0I7Q4NIAAAAAAAWl23zp1GD39MRIxGY9yug7W1dUonQvvSvF3APv/8cxEJCwtrnTAAAABWh/oHAGApE8Ifz8q+mld4q6Ts9s79x2ZNflLpRGhHmtcAWrJkSSvlAAAAsE7UPwAAS3HQaOY9Pe7jDfE6nf7E6fOP9Ow6oE8PpUOhveAVMAAAAAAA2kgnP5/xox43HSfsTamorFY2D9oPGkAAAAAAALSdMcMf69E1UEQqq2ri96YoHQftBQ0gAAAAAADajkqlmj01wknrKCJnf7n8c2aW0onQLtAAAgAAAACgTXXw8pgS+ev2At8lHSktr1A2D9oDGkAAAAAAALS14YMf7dszWERqauvidiUbjUalE8HO0QACAAAAAEAB0VPGuLu6iEj2lRtHfjqjdBzYORpAAAAAAAAowN3VJeqp0abj3QePFxTeUjYP7BsNIAAAAAAAlNG/T/fB/XqJiE6n35yYrDcYlE4Eu0UDCAAAAAAAxTwzMdzb011ErhcUHThyUuk4sFs0gAAAAAAAUIyzkzZ68hiViIjsP3oy98ZNhQPBTtEAAgAAAABASb26dx4R2l9EDAbD5l3J9Tqd0olgh2gAAQAAAACgsMkRw/07eItIYXHpnuRUpePADtEAAgAAAABAYY6ODvOmj9Wo1SJy5KfMrJxcpRPB3tAAAgAAAABAeZ0D/CLCBouIUWTLnkPVNbVKJ4JdoQEEAAAAAIBVGDcqtEugv4iU367ckXRE6TiwKzSAAAAAAACwCmq1es7USEcHBxH5+cwvGedzlE4E+0EDCAAAAAAAa9HR13vik0+Yjrd9/+Ptyipl88Bu0AACAAAAAMCKhD8xsGdwkIhUVtfEJSYblc4D+0ADCAAAAAAAK6JSqeZMi3RxdhKRrEvX0tLPK50I9oAGEAAAAAAA1sXLw23q2DDT8Y4fjhSXlCubB3aABhAAAAAAAFbniYGPDHwkRETq6nWbdyUbjLwKBrPQAAIAAAAAwBo981S4u5uLiFy5lv9j6mml48C20QACAAAAAMAaubk4z5z0pOn4+5S0vMJbyuaBTaMBBAAAAACAlerXq9vjA/uIiE6v37zzgF5vUDoRbBUNIAAAAAAArNfT40b6eLqLSN7N4qTDPykdB7aKBhAAAAAAANbLyUk7d/pYtUolIsnHTl3KzVM6EWwSDSAAAAAAAKxa9y4Bo54YKCJGozFu18HaunqlE8H20AACAAAAAMDaPTVmaIB/BxG5VVq+K/m40nFge2gAAQAAAABg7Rw0mjlTIzQatYiknjx7Ifuq0olgY2gAAQAAAABgA4I6+Y0dGSoiRpEtew5VVdfo9HrTRwajUdFosAE0gAAAAAAAsA2RYYODgzqKyO2KqvfWxl65XmA6v/p/N+9IOlJVU6toOlg1GkAAAAAAANgGtVo9e2qEWq0WkTuXgq6pqz/8U+Zf/7m1uKRcuXSwajSAAAAAAACwGSfPXDQYDPf8qKy8YkP8Xv19PkU7RwMIAAAAAADbUFtbl5J6+gEDbhaVnD6X3WZ5YENoAAEAAAAAYBsuXrlRr9M9eMw5NgjDvdAAAgAAAADANpSU3X74mNKHj0E7RAMIAAAAAADboFKrLDIG7ZCD0gEerqysLDY2NjU1tbS01NfXd8SIEfPmzXN1dbXgteZM0aCmpmbJkiWjRo1avnx5sy4EAAAAAKAp/Hy8HjrGvwlj0A5ZewOouLh41apVRUVFIuLs7FxQULBt27a0tLT333/f3d3dItc2cVh6evqmTZtycnL8/PzCw8Ojo6MdHR3vnG7Hjh3V1dVz5syx4O0DAAAAANCgZ3CQi7NTdU3tA8b0f6RHm+WBDbH2V8A+++yzoqKikJCQTz/99Ntvv129enXHjh2vXbv21VdfWerapgxLTk5+6623srOze/XqVVtbGxsb+/bbbxuNxoYBlZWV8fHxU6ZM8fX1teDtAwAAAADQwMFBM3H0Ew8YENI18NFe3dosD2yIVTeAysrKUlNTtVrtG2+80bVrV5VK1bNnzzfffFNEDh06VFdXZ/61TRlWV1e3bt26gICAtWvX/uUvf1m3bt3kyZNPnTp15MiRhum2bdum0+mio6Nb73cDAAAAAIARof3HDH/snsv8dAnwnx81gRWAcE9W3QBKSUnR6/WDBg3q1KlTw8mQkJCQkJCqqqq0tDTzr23KsMuXL1dWVkZFRXXo0EFENBrNwoULRSQzM9M0vry8fPv27dOnT/fy4k1LAAAAAEDrmhwxfMmz0x7pGaxW/drt6eTnM338yOXPz3BzcVY2G6yWVTeAMjIyRGTIkCGNzoeGhopIenq6+de2eAqVSqXX603H8fHxarU6KirqwbcDAAAAAIBF9AwOWhQ9qUfXQNMvl8ydOurxARqNVf8dH8qy6n85SkpKRCQoKKjR+cDAQBEpLS01/9qmDOvevburq2tCQkJxcbGI6PX6DRs2GI3GgQMHmr5h586dM2bM8PDwaNFdAgAAAAAAtC6r3gWsrKxMRNzc3BqdN23OZfrUzGubMkyr1S5btuyjjz5avnx57969CwoKbt68GRoaGh4eLiJxcXFarXbGjBlNvCmj0ajT6e73kcFgaOL3AAAaaVib32Aw8Mdpu8I/bgAAgIeygQbQ3du9N70B9NBrmzgsIiLC29t706ZNFy9e9PX1ffbZZ2fPnq1SqQoLC/fs2fPcc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width="768" /></p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/supporting_information_figure3.png'</span>, <span class="dt">plot =</span> g2,</a>
<a class="sourceLine" id="cb26-2" data-line-number="2">          <span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">8</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a></code></pre></div>
</div>
<div id="topic-clusters-and-correlations-figure-3" class="section level3">
<h3>Topic clusters and correlations (Figure 3)</h3>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1"><span class="co"># hierarchical clustering</span></a>
<a class="sourceLine" id="cb27-2" data-line-number="2">stm_dist &lt;-<span class="st"> </span><span class="kw">CalcHellingerDist</span>(stm_<span class="dv">30</span><span class="op">$</span>theta, <span class="dt">by_rows =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb27-3" data-line-number="3">clusters &lt;-<span class="st"> </span><span class="kw">hclust</span>(<span class="kw">as.dist</span>(stm_dist), <span class="st">&quot;ward.D2&quot;</span>)</a>
<a class="sourceLine" id="cb27-4" data-line-number="4">clusters<span class="op">$</span>labels &lt;-<span class="st"> </span>topic_labels</a>
<a class="sourceLine" id="cb27-5" data-line-number="5"></a>
<a class="sourceLine" id="cb27-6" data-line-number="6"><span class="co"># create dendrogram</span></a>
<a class="sourceLine" id="cb27-7" data-line-number="7">dendro &lt;-<span class="st"> </span><span class="kw">ggdendrogram</span>(clusters, <span class="dt">rotate =</span> <span class="ot">TRUE</span>, <span class="dt">size =</span> <span class="dv">50</span>)  <span class="op">+</span></a>
<a class="sourceLine" id="cb27-8" data-line-number="8"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">size =</span> <span class="dv">10</span>),</a>
<a class="sourceLine" id="cb27-9" data-line-number="9">        <span class="dt">axis.title =</span> <span class="kw">element_text</span>(<span class="dt">size =</span> <span class="dv">14</span>, <span class="dt">face =</span> <span class="st">&quot;bold&quot;</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-10" data-line-number="10"><span class="st">  </span></a>
<a class="sourceLine" id="cb27-11" data-line-number="11"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="ot">NULL</span>, <span class="dt">x =</span> <span class="st">'Distance'</span>)   <span class="op">+</span><span class="st"> </span><span class="kw">scale_y_continuous</span>(<span class="dt">labels =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb27-12" data-line-number="12"></a>
<a class="sourceLine" id="cb27-13" data-line-number="13">df<span class="op">$</span>dummy_nonwhite &lt;-<span class="st"> </span><span class="kw">rec</span>(df<span class="op">$</span>dummy_white, <span class="dt">rec =</span> <span class="st">'rev'</span>)</a>
<a class="sourceLine" id="cb27-14" data-line-number="14"></a>
<a class="sourceLine" id="cb27-15" data-line-number="15"></a>
<a class="sourceLine" id="cb27-16" data-line-number="16"><span class="co"># create correlation matrix</span></a>
<a class="sourceLine" id="cb27-17" data-line-number="17">cormat &lt;-</a>
<a class="sourceLine" id="cb27-18" data-line-number="18"><span class="st">  </span><span class="kw">cbind</span>(stm_<span class="dv">30</span><span class="op">$</span>theta,</a>
<a class="sourceLine" id="cb27-19" data-line-number="19">        df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(dummy_female, dummy_nonwhite,</a>
<a class="sourceLine" id="cb27-20" data-line-number="20">                      date_num)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb27-21" data-line-number="21"><span class="st">  </span><span class="kw">corr.test</span>()</a>
<a class="sourceLine" id="cb27-22" data-line-number="22">cormat &lt;-<span class="st"> </span>cormat<span class="op">$</span>r</a>
<a class="sourceLine" id="cb27-23" data-line-number="23"></a>
<a class="sourceLine" id="cb27-24" data-line-number="24">pmat &lt;-</a>
<a class="sourceLine" id="cb27-25" data-line-number="25"><span class="st">  </span><span class="kw">cbind</span>(stm_<span class="dv">30</span><span class="op">$</span>theta,</a>
<a class="sourceLine" id="cb27-26" data-line-number="26">        df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(dummy_female, dummy_nonwhite,</a>
<a class="sourceLine" id="cb27-27" data-line-number="27">                      date_num)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb27-28" data-line-number="28"><span class="st">  </span><span class="kw">corr.test</span>()</a>
<a class="sourceLine" id="cb27-29" data-line-number="29">pmat &lt;-<span class="st"> </span>pmat<span class="op">$</span>p</a>
<a class="sourceLine" id="cb27-30" data-line-number="30"></a>
<a class="sourceLine" id="cb27-31" data-line-number="31"></a>
<a class="sourceLine" id="cb27-32" data-line-number="32"><span class="kw">colnames</span>(cormat) &lt;-<span class="st"> </span><span class="kw">c</span>(topic_labels, <span class="st">'female'</span>, <span class="st">'non-white'</span>, <span class="st">'date'</span>)</a>
<a class="sourceLine" id="cb27-33" data-line-number="33"><span class="kw">rownames</span>(cormat) &lt;-<span class="st"> </span><span class="kw">c</span>(topic_labels, <span class="st">'female'</span>, <span class="st">'non-white'</span>, <span class="st">'date'</span>)</a>
<a class="sourceLine" id="cb27-34" data-line-number="34"></a>
<a class="sourceLine" id="cb27-35" data-line-number="35">cormat &lt;-<span class="st"> </span>cormat <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">melt</span>()</a>
<a class="sourceLine" id="cb27-36" data-line-number="36">pmat &lt;-<span class="st"> </span>pmat <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">melt</span>()</a>
<a class="sourceLine" id="cb27-37" data-line-number="37">pmat<span class="op">$</span>pstar &lt;-</a>
<a class="sourceLine" id="cb27-38" data-line-number="38"><span class="st">  </span><span class="kw">case_when</span>(</a>
<a class="sourceLine" id="cb27-39" data-line-number="39">    <span class="kw">is.na</span>(pmat<span class="op">$</span>value) <span class="op">~</span><span class="st"> &quot;&quot;</span>,</a>
<a class="sourceLine" id="cb27-40" data-line-number="40">    pmat<span class="op">$</span>value <span class="op">&lt;</span><span class="st"> </span><span class="fl">0.001</span> <span class="op">~</span><span class="st"> &quot;***&quot;</span>,</a>
<a class="sourceLine" id="cb27-41" data-line-number="41">    pmat<span class="op">$</span>value <span class="op">&lt;</span><span class="st"> </span><span class="fl">0.01</span> <span class="op">~</span><span class="st"> &quot;**&quot;</span>,</a>
<a class="sourceLine" id="cb27-42" data-line-number="42">    pmat<span class="op">$</span>value <span class="op">&lt;</span><span class="st"> </span><span class="fl">0.05</span> <span class="op">~</span><span class="st"> &quot;*&quot;</span>,</a>
<a class="sourceLine" id="cb27-43" data-line-number="43">    <span class="ot">TRUE</span> <span class="op">~</span><span class="st"> &quot;&quot;</span></a>
<a class="sourceLine" id="cb27-44" data-line-number="44">  )</a>
<a class="sourceLine" id="cb27-45" data-line-number="45">cormat &lt;-<span class="st"> </span><span class="kw">bind_cols</span>(cormat, pmat)</a>
<a class="sourceLine" id="cb27-46" data-line-number="46">cormat &lt;-</a>
<a class="sourceLine" id="cb27-47" data-line-number="47"><span class="st">  </span>cormat <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(Var1 <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">'female'</span>, <span class="st">'non-white'</span>, <span class="st">'date'</span>))</a>
<a class="sourceLine" id="cb27-48" data-line-number="48">cormat &lt;-</a>
<a class="sourceLine" id="cb27-49" data-line-number="49"><span class="st">  </span>cormat <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>Var2 <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">'female'</span>, <span class="st">'non-white'</span>, <span class="st">'date'</span>))</a>
<a class="sourceLine" id="cb27-50" data-line-number="50">cormat<span class="op">$</span>Var2 &lt;-<span class="st"> </span><span class="kw">as.character</span>(cormat<span class="op">$</span>Var2) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb27-51" data-line-number="51"><span class="st">  </span><span class="kw">factor</span>(<span class="dt">levels =</span> topic_labels[clusters<span class="op">$</span>order], <span class="dt">ordered =</span> T)</a>
<a class="sourceLine" id="cb27-52" data-line-number="52"></a>
<a class="sourceLine" id="cb27-53" data-line-number="53">cormat &lt;-<span class="st"> </span>cormat <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb27-54" data-line-number="54"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">pstar  =</span> <span class="kw">if_else</span>(</a>
<a class="sourceLine" id="cb27-55" data-line-number="55">    value <span class="op">&lt;</span><span class="st"> </span><span class="dv">0</span>,</a>
<a class="sourceLine" id="cb27-56" data-line-number="56">    <span class="kw">str_replace_all</span>(pstar, <span class="st">'</span><span class="ch">\\</span><span class="st">*'</span>, <span class="st">'-'</span>),</a>
<a class="sourceLine" id="cb27-57" data-line-number="57">    <span class="kw">str_replace_all</span>(pstar, <span class="st">'</span><span class="ch">\\</span><span class="st">*'</span>, <span class="st">'+'</span>)</a>
<a class="sourceLine" id="cb27-58" data-line-number="58">  ))</a>
<a class="sourceLine" id="cb27-59" data-line-number="59"></a>
<a class="sourceLine" id="cb27-60" data-line-number="60"><span class="co"># create visualization</span></a>
<a class="sourceLine" id="cb27-61" data-line-number="61">topcors &lt;-<span class="st"> </span><span class="kw">ggplot</span>(cormat, <span class="kw">aes</span>(Var1, Var2)) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-62" data-line-number="62"><span class="st">  </span><span class="kw">geom_tile</span>(<span class="kw">aes</span>(<span class="dt">fill =</span> value), <span class="dt">color =</span> <span class="st">'white'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-63" data-line-number="63"><span class="st">  </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> pstar)) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-64" data-line-number="64"><span class="st">  </span><span class="kw">scale_fill_gradient2</span>(</a>
<a class="sourceLine" id="cb27-65" data-line-number="65">    <span class="dt">low =</span> <span class="st">&quot;#0571b0&quot;</span>,</a>
<a class="sourceLine" id="cb27-66" data-line-number="66">    <span class="dt">mid =</span> <span class="st">'white'</span>,</a>
<a class="sourceLine" id="cb27-67" data-line-number="67">    <span class="dt">high =</span> <span class="st">&quot;#ca0020&quot;</span>,</a>
<a class="sourceLine" id="cb27-68" data-line-number="68">    <span class="dt">midpoint =</span> <span class="dv">0</span>,</a>
<a class="sourceLine" id="cb27-69" data-line-number="69">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="op">-</span><span class="fl">0.3</span>, <span class="fl">0.3</span>),</a>
<a class="sourceLine" id="cb27-70" data-line-number="70">    <span class="dt">breaks =</span> <span class="kw">c</span>(<span class="op">-</span><span class="fl">0.3</span>, <span class="fl">-0.15</span>, <span class="dv">0</span>, <span class="fl">0.15</span>, <span class="fl">0.3</span>)</a>
<a class="sourceLine" id="cb27-71" data-line-number="71">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-72" data-line-number="72"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb27-73" data-line-number="73">    <span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">size =</span> <span class="dv">10</span>),</a>
<a class="sourceLine" id="cb27-74" data-line-number="74">    <span class="dt">panel.grid.major =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb27-75" data-line-number="75">    <span class="dt">panel.grid.minor =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb27-76" data-line-number="76">    <span class="dt">legend.position =</span> <span class="st">'right'</span>,</a>
<a class="sourceLine" id="cb27-77" data-line-number="77">    <span class="dt">legend.direction =</span> <span class="st">'vertical'</span>,</a>
<a class="sourceLine" id="cb27-78" data-line-number="78">    <span class="dt">plot.title =</span> <span class="kw">element_text</span>(<span class="dt">hjust =</span> <span class="dv">1</span>)</a>
<a class="sourceLine" id="cb27-79" data-line-number="79">  )  <span class="op">+</span></a>
<a class="sourceLine" id="cb27-80" data-line-number="80"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="ot">NULL</span>,</a>
<a class="sourceLine" id="cb27-81" data-line-number="81">       <span class="dt">x =</span> <span class="ot">NULL</span>,</a>
<a class="sourceLine" id="cb27-82" data-line-number="82">       <span class="dt">fill =</span> <span class="st">'Correlation'</span>,</a>
<a class="sourceLine" id="cb27-83" data-line-number="83">       <span class="dt">title =</span> <span class="ot">NULL</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-84" data-line-number="84"><span class="st">  </span><span class="kw">scale_x_discrete</span>(</a>
<a class="sourceLine" id="cb27-85" data-line-number="85">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">00</span>),</a>
<a class="sourceLine" id="cb27-86" data-line-number="86">    <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'Gender:</span><span class="ch">\n</span><span class="st">female'</span>, <span class="st">'Ethnicity:</span><span class="ch">\n</span><span class="st">non-white'</span>,</a>
<a class="sourceLine" id="cb27-87" data-line-number="87">               <span class="st">'Date'</span>)</a>
<a class="sourceLine" id="cb27-88" data-line-number="88">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb27-89" data-line-number="89"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">face =</span></a>
<a class="sourceLine" id="cb27-90" data-line-number="90">                                     <span class="kw">c</span>(<span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">10</span>), <span class="st">'bold'</span>, <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">26</span>))))</a>
<a class="sourceLine" id="cb27-91" data-line-number="91"></a>
<a class="sourceLine" id="cb27-92" data-line-number="92"></a>
<a class="sourceLine" id="cb27-93" data-line-number="93">g &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(</a>
<a class="sourceLine" id="cb27-94" data-line-number="94">  dendro,</a>
<a class="sourceLine" id="cb27-95" data-line-number="95">  topcors,</a>
<a class="sourceLine" id="cb27-96" data-line-number="96">  <span class="dt">align =</span> <span class="st">'hv'</span>,</a>
<a class="sourceLine" id="cb27-97" data-line-number="97">  <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'A'</span>, <span class="st">'B'</span>),</a>
<a class="sourceLine" id="cb27-98" data-line-number="98">  <span class="dt">hjust =</span> <span class="fl">0.0</span></a>
<a class="sourceLine" id="cb27-99" data-line-number="99">)</a>
<a class="sourceLine" id="cb27-100" data-line-number="100">g</a></code></pre></div>
<p><img 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8cOJZOAFRUVzNFDPB7v1KlT3cgeUmvXrqVZQkpdXX3r1q0aGhrNzc03btzoUsxTpkxRU1NrbGykyThKca/u7du3CSHjx49nsofUzJkz6bGGcoV6XF1dmewhNXnyZEIIu+RjD38HunT79evXCSGOjo5M9pCaP3++paWl3MhKzglbQUFBrALU1wYAgD7yn7/aFfcvK+/WrVsVFRW9EQ8AAAAADEilpaVM4RFKLpujiCZHJk2aJFfnhBCiq6s7atQoQohiQmTMmDHsh0zS0MrKit3O5XJ5PB675cWLF4SQWbNmKZZ2cXJyoknJrpYn7t6YU6ZMkWthr7KkzM3NCSGJiYnR0dESiYRpd3BwWLhw4YQJEzqNLSkpaceOHY8ePTIxMVFTU5NIJIGBgczVrKysBw8e1NTUdDoOIYTH4zk7O8s16unpTZ06lRDy/PnzLsWsra1tZ2dHCElOTmb60KV25ubm9H1nhnVxcZF7Xg6Hc/jwYT8/P7lpVJxVxYIkPfwd6NLtz549I4TMmTNHMX7FF6XknMi9umEKFF8yAABAr/jX33xPnz598OAB+8KlS5c2bNjQ3m35+fnvvvsu87ClpeWnn37avHlzH0UJAAAAAK+zlJSU//mf/6msrGQ3yp3mpkggEBBCIiMjIyMj2+tTV1cn18JeZqhMO0MoFBJC2PU0GFwu18jIqKSkhPZRXvfGbLO/HHd399jY2Nra2jNnzly4cGHq1Knjx4+3t7dX5l5CSEJCgq+vr1QqdXFx8fT0jI6O/uc//xkTE2Ntbe3q6iqTyQ4fPiyRSM6ePatMysnExESx2jL5d8aQeYHKxzx9+vTMzMyUlBSZTMZsWCaEzJo1iz5sbm6mCxTafL1tNrLrz7Snh78Dyt9eW1tbX1/fXlRtNnY6J3KMjY0VC0xXVlbSQtvwxigXlTU0yP9v0MDQeMiQdzq9CgDQi/6VQAwODma3qqmp/fnPf+7gNhsbG2tra3qoBxUSEoIEIgAAAMAb7ObNm9OnT5drbGxsLC4uZu+9pXg8Ht3U2QGRSEQIUVNTazM5RTE7cHuotbWV7mUeOnRomx2GDh1aUlJCQ+rrMTvNdRJC9PX1v/vuu4CAgISEhIaGhjt37ty5c4cQYmVltWLFCicnpw4Oxauvrz99+rRUKnV1dfX09ORwOMuWLcvLy7t///73338/atQoHR0diUTC5XKVTEeyNy8rtjPlpJWPefr06d9//311dXVeXp6tra3iXt2Kigp6vFJ7c6uo01nt4e9Al25n5qTNzm02djonAxTH5k+Kjd8Hh3wf/F+nYD38OQx1mdt04oj3rz+HyjUePXnufbdVnV4FAOhFbScQ33vvvfY+JTCWLl168uRJ5uHt27fLy8sNDQ17PUQAAAAAeB1UVlay91d2zMvLiy5P64Cenl5VVdX69etXrlzZ4+g6weVydXV1xWIx+0Q8Npob0tPTU+2YbHp6env37t2+ffvDhw+zsrKysrL4fH5BQYGPj8/q1avXrVvX3o337t1raGjQ09PbsmULzdlxOJxdu3YVFxfz+fxjx47R3b7jxo3rIHXL1t5OZ5o4ZqfDlIzZwMDAxsYmPz8/OTnZ1taWLrUbPXo08zvDrItUXILabT18v7p0OzMnYrFYcZ1gm/PZ6ZwAAACokBoh5PHjx48fP2a3Llu2rNM7ly5dyn7Y2tpKzwkGAAAAgLfcRx999Pe//73TbsOGDSP/PleuH9B9o21uUJVKpbQACA1JtWPK4fF4M2bM2LZt25kzZ06ePDlx4kRCSEhICN0h2ya6N9zW1nbQoEHscf7+978PHjy4vLycFst2d3dXMgahUNhmuUV63p/iC1QmZrqaNSkpibD26jJXtbS0dHV1STtzW1xcnJSU1I2CIT18v5S/fejQofQIzjY7t7dLuuM5GaCaczLZf777yosQsnXVSrl2e9t3Ox3q7XTs1PePCivl/jALDDu+CgDQi9SIwvJDopAcbJOTkxP9S52BWswAAAAAbzkjI6Pvv//+xx9/VKyLoogurcrIyGAX3KDq6+v37NmzY8eOgoKC3oqNFnWJj49vbW2Vu5SQkED3Snd1tVdfjEkIaW5u/uKLL/bv35+ens5ut7Ky2r59OyFEJpPRLGGbaIkPxWVu5ubme/bsoT8bGRnRIsXKqK2tTUtLk2usrq6mqS5aUbqrMdPayiUlJcnJyXSvrlydFjosLXws59y5c0eOHFF+PSyjh++X8rdzOJzhw4cTQugmbjaZTHb//v02x+90TgYidS6X/YerxiWEqKmpybV3sCUfAABeB2oymUwugWhra2tjY9PpnRoaGosXL2a3xMbGdunUGAAAAAAY6LhcroGBwdixYzdu3Hjp0qXnz59//PHHSuYC6OqqysrKoKAg9gI3mUwWGBhYWFhYW1s7evTo3gqVPl1ZWdm1a9fY7dXV1VevXiXtlLuVSqW9PmanNDQ0Kisrc3Jybt68Kbf0jymR0cFCOUtLS0JIXl5eSUmJ3CV69CEhRCQStZfGatPly5dra2uZhy0tLefPn29qauJyufQfBV2N2dzcnKbYzp49SwgZO3askZER+y6aO0tLS5PLSObm5mZnZxNCxo8fr3z8VA/fry7dTjd1paSkZGZmsjvfu3evvbR4p3MCAACgKuocDqfb3+sGBQUFBQX1bkAAAAAA8Jpo76y33mJpablw4cKYmJjr16/z+fwFCxYMGzasqqoqJiaG7t/csGGDMisZlTR58uQJEyZkZWVdvnz5xYsXTk5Oenp6BQUFoaGhtOTF5s2b2alP+vPjx48rKio0NDTarFbc1TGVN2XKlMjIyISEhNOnT8+ZM2fo0KF1dXVZWVnh4eGEEDs7uw5qhtjZ2fF4PIlEcuzYsc8//5zmpJqbm4OCgsLCwggh2tratbW1p0+fVvKIPQ6H8+zZs3379i1fvnzkyJElJSU3b97MyckhhCxatIg546+rMU+fPp3P59Pi3Yp7defPn//rr7/y+fxvvvlmxYoVEydO1NLSysnJCQ4Olslk48ePpzuju6SH71eXbnd2dg4LCysuLj506JCHh8eECRNkMllaWtr169fNzc1pblfx17vjOQEAAFAVdVUHAAAAAABvr48//ri6ujolJSU1NTU1NZVp53A4q1atmj9/fi8+F4fD2bdv36FDh54+fRoXFxcXF8e+tGXLFkdHR3Z/S0vLjIyMR48ebdq0ycbG5sSJEz0fU3kbNmzIyMjg8/m3bt26desW+5Kpqem+ffs6uNfAwGDDhg3nz58vLi7etWvXqFGjtLW1CwoK6BLCpUuXfvDBB7t37xaLxcePHz9x4oSmpmbHwUyePHnQoEEJCQl0ZRxj3LhxGzZs6HbMM2bMCA0NJYRwOBwnJye5q5qamt7e3l9++WVZWVlYWBhNfTKj7d27txvJ5R6+X126XU1Nbd++fV5eXmKxODg4mNn1ZWZm5u3t/cknn/B4PMWZ73hOAAAAVAUJRAAAAABQGU1NTS8vr/v379++ffvJkycSicTU1HTkyJEffPBBL25eZujq6vr6+kZHR9+9e5fP50skEj09PTs7u+XLlys+3erVq0UiUWZmplQq7WAnaZfGVB6Pxzt16lR0dHR8fLxIJBKLxfr6+iYmJs7OzvPmzdPQ0Oj49vfff3/QoEE//vhjQ0MDs99IS0try5Yt8+bN43A4u3fv/uabb4qKin744YcdO3Z0PBqXy/38889jYmKio6P5fD6Hw7G0tJwzZ86iRYvYdZy7GvOYMWMMDQ3Ly8vt7e3ZpZwZpqamp0+fjoiISEhIEAgEmpqaw4cPd3BwcHNz6zTp2Z4evl9dut3S0vK7774LCQlJSkqqqqrS09NzcnJas2YNPZ6yzZfc6ZwAAACoBKfNemoA8LppamqKiYlxcHCgp3cDAAAoqbW1lZbcnTBhAj0aD94edXV1T58+LS4ubm1tNTU1nTRpEi0NrLyLFy+Gh4dPmzbNy8ur18NramrauHFjfX39rl27ene16Wvujz/+OHDggK2trY+Pj9ylHs5JZWVlYmLirJHmOoO6mWAFQgjRNypu0aptaFZ1HAObmhpnnOkg8qpB1YEMbDHJqU3NzTY2NspUqgDoU1iBCAAAAADwZnrnnXfs7e3t7e1VHUjb0tLS6uvr1dXVafXhN0xkZOTTp0/t7OzmzZsnd+nhw4eEkDaXtb7ZcwIAAANXrx1KDQAAAAAAoCSZTPbLL78QQiZPntxBQZiBSyKRxMbGBgcHNzU1sdtra2ujoqIIIQ4ODnK3vPFzAgAAAxdWIAIAAAAAQP/Jzs6uqqrKzMx89OgRIWTJkiWqjqhPODk5hYSECIXCo0ePbtq0afjw4a9evcrPz/+///u/uro6fX19do2Ut2ROAABg4EICEQAAAAAA+k9CQkJkZCT9ecqUKa/tDuseMjMz27Vr18mTJ9PS0tLS0rhcbmtrK72kqan56aefss+jfEvmBAAABi4kEAEAAAAAoP8MHz5cT0+vtbXV2dl548aNHA5H1RH1FWdnZ2tr65CQkCdPnpSWlqqrqxsYGNjb27u7u5uamrJ7vj1zAgAAAxSqMLcrLi7u0qVLhJDNmze/9957qg5HKSKRKCgoKC8vTyKRjBkzZtWqVVZWVqoOqjcJBIJt27YRQq5cuaKjo9OLIxcWFu7Zs4cQEhYWpqn5OhatQxVmAADoHlRhBnh7/KsK88QJOkO0VB3LQDZosFiq0dQqVXUcA5saIQZDuKS1RdWBDGwxd+6hCjO8JrAC8V+8vb0zMzPXrVu3evVq2iKRSCoqKgghjY2NKg1NWdnZ2YcOHZJIJPShSCRKSUn57LPPBkr2EwAAAAAAeu5ZA3eQFP/Q6z4Djlp1nURc39R5V2ifupqagQmHVFWoOpABToZENrwuUIX5DdHY2Hjq1CmJRDJ//vyAgICLFy+6urpKpdJz5841NDSoOjoAAAAAAAAAABio8MVUuxYvXrx48WJVR6Gs/Px8oVCoq6vr6emprq5OCPnkk08yMzNLS0vz8/MnTpyo6gABAAAAAAAAAGBAwgrEN0RJSQkhxMTEhGYPCSFcLnf48OGEkJcvX6oyMgAAAAAAAAAAGMhUsAIxPT39xo0bubm5dXV1hoaG1tbWS5cutbW1ZfcpLy//61//qq2tHRgYWFBQEBwcnJOT09jYOGzYsPfff3/hwoUcDicnJyc0NDQvL6+pqcnc3NzV1dXV1VVN7b9Soq2trdHR0Xfv3uXz+RKJRE9Pz87ObtmyZaNHj2b6+Pn5xcbG0p8DAwMDAwNtbGxOnDjRXr0OZcYk/y73QV9CVVVVSEjIgwcPqqqqjIyMxo0bt3btWkNDQ7mZyc7O/vnnn58/fy4SiXR1dc3MzJYsWTJt2jRlqrCNGDGCEFJUVNTc3KyhoUEIaW5uLiwsJITo6el1/q4QQgi5ePFieHj4hx9+uGbNmrt370ZFRRUXF3M4HAsLi7lz57q6unK53C7FnJSUdOTIEQ6Hc/nyZcWaJzt37iwuLl6yZAmdZxpzVFTUvXv3SkpK1NTUzM3NHR0d3dzcBg0apBhteXl5UFBQenp6TU2NoaHh2LFj165da2xsLNdNyferPV26vaGh4dq1a4mJiUKhcMiQIQ4ODmvWrDEyMnJ3dyeEhISE8Hi8rs4JAAAAAAAAAIBq9WsCUSqVnj17Njo6mmkpLS0tLS2Nj4/38PBYv369YqYsNTX1yJEjLS3/qtxUXFx85syZ2tpafX19f39/poR0YWHh2bNnKyoq1q9fz9xbU1Nz6NChgoICpkUkEsXGxsbFxW3atMnNzY02vvPOOwYGBmKxuKWlZfDgwVpaWrq6uu29BCXHZBMIBF5eXuXl5fThy5cvX758mZiY6O/vb2JiwnQLCAi4du0ae1iRSJSZmblo0SJPT8/24mFYWVnp6OjU1NRcuXJl06ZNTU1N/v7+FRUV2traXd2/LJPJzpw5ExMTw7Tk5+fn5+fHx8cfPHhw8ODBysc8adIkTU3Npqam1NTUuXPnsp+Fz+cXFxcTQmbNmkVbqqurDx48WIcfgPsAACAASURBVFRUxPTJy8vLy8uLiYnx9fXV19dn315UVHT8+PHa2lr6UCAQCASCxMTEb7/91tTUlOnWjfeLrUu3C4VCLy+vsrIy5uXExsYmJyd/9dVX7G5dmhMAAAAAAAAAAJXr1wRiUFAQzR7OmDHDxcXF2NiYz+dHREQUFBSEhobq6OgsX76c3V8ikfj6+o4aNeqjjz6ytLTMy8s7c+ZMVVVVYGCgTCYzMzPbsmWLlZVVUVHRmTNnSktLr1275uHhQVeryWQyX19fmvqZPXu2k5OTgYFBQUFBeHh4WVnZjz/+aGFh4ejoSAjZsmXLli1baBXmFStWMFWYFSk/JqO1tdXX1/fVq1c7d+60t7eXSqVxcXHBwcENDQ0BAQH79++n3VJTU2kmztHRccmSJYaGhhUVFREREXS1ppOTk729fcdzq6GhsWXLlpMnT16/fn3EiBFRUVH5+flcLnf37t3slJ8y4uLihEKhoaGhm5ublZXVy5cvb968mZOT88cff1y6dIlZGadMzIMGDXJwcEhKSkpOTpZLliUkJBBCjIyM6OJTqVR69OjRoqIiDofj7u7u4OCgqan58OHD8PBwkUjk5+f39ddfs/PLx48fJ4Ts3r3bzs5OKpXGxMSEhYVJJJKLFy8ys9qN94utS7fLZDIfH5+ysjIul7tixYqJEydKpdKUlJTIyMhjx46xh1V+TgAAAADeYIWFhXv27CGEhIWFaWpq9s+TxsXFXbp0iRCyefPm9957r3+eFAAA4A3QfwnE8vLy0NBQQsi6deuYJN2YMWOcnZ19fHx+//33q1evzp07V1tbm7mlubnZ2Nj46NGj9CPF1KlTm5qafHx8Wltbhw4deuLEiSFDhhBC7O3td+7c6eXl1dLSIhAIRo4cSQhJT0/PzMykT7dq1Sqae7K2tp45c+aBAwdevHhx4cKFyZMnK7M7mNGNMRsaGoqLi/39/S0sLGjLhx9+KBaLo6KicnNzmW6pqal0Nr788ku6C3vEiBF2dnbbtm0rLy/PysrqNIFICJk9e/bdu3fT0tL8/PwIIfr6+nv37lXmRjlCodDU1PTo0aMGBgaEkHHjxrm4uJw8efL+/fsxMTErV66k7UrGPH369KSkpPT09KamJvZHQ5osc3Z2ptOVkJCQk5NDCPniiy9mzJhB+9ja2lpaWh4/fjwjI+PFixf0SEeqsbHx22+/NTMzow//8pe/VFdX37p1Kz8/n+nTw9+BLt2empr65MkTQoi3t7eDgwMdwd7efuTIkadPn5YbWck56TsnT57My8vr06cAeP2dOXOGOTQWAADeEhKJpKKighDS2Nio6lgAAAAGkv77t9OdO3ekUqmFhYWHhwe7ncvlenp6Pnjw4NWrV48ePZo5cyb7qoeHBzvDMn78ePqDu7s7zR5S48aNU1NTk0qlYrGYtsTHxxNCDA0NP/jgA3Y6RldXd82aNSdOnODz+UVFRaNGjVL+JXRvTFdXVyZ7SDk6OkZFRVVVVTEtdN+rhYUF+wxHDQ2NHTt2lJeXs/fkdqCioqKpqYn+zOPxTp06pfzph3LWrl1Ls4SUurr61q1bk5OTm5ubb9y4sW7dOuVjnjJlipqaWmNjY2Zm5pQpU2ij4l7d27dvE0LGjx/PZA+pmTNnGhsbl5WVZWdnsxOIrq6uTPaQmjx58q1bt6qrq5mWHv4OdOn269evE0IcHR2Z7CE1f/58ekYku1HJOek7v/76K3PuJ8Bba+/evcOGDevgzAoAAAAAAACg+q8KM02OTJo0Sa7OCSFEV1eXZmHYy8eoMWPGsB8ySUMrKyt2O5fL5fF47JYXL14QQmbNmqW4wMTJyYkmJbtanrh7YzIZIgZ7lSVlbm5OCElMTIyOjpZIJEy7g4PDwoULJ0yY0GlsSUlJO3bsePTokYmJiZqamkQiCQwMZK5mZWU9ePCgpqam03EIITwez9nZWa5RT09v6tSphBAmF6ZkzNra2nZ2doSQ5ORkpg9damdubs4k7+iwLi4ucs/L4XAOHz7s5+cnN42Ks6pYkKSHvwNduv3Zs2eEkDlz5ijGr/iilJwTAOhTY8eO/X//7/+pOgoAAOhXixcvjoiIiIiImDdvnqpjAQAAGEj6bwWiQCAghERGRkZGRrbXp66uTq6FvcxQmXaGUCgkhLS5do/L5RoZGZWUlNA+yuvemMqsH3R3d4+Nja2trT1z5syFCxemTp06fvx4e3t7JdceJiQk+Pr6SqVSFxcXT0/P6Ojof/7znzExMdbW1q6urjKZ7PDhwxKJ5OzZs4pZNkUmJiaK1ZbJvzOGzAtUPubp06dnZmampKTIZDJmwzIhZNasWfRhc3Mz3UvS5utts5Fdf6Y9PfwdUP722tra+vr69qJqs7HTOelTvr6+lZWVff0sAK+tqKioU6dOqToKAAAAAACAAaP/EogikYgQoqam1mZyimJ24PZQa2sr3cs8dOjQNjsMHTq0pKSEhtTXY3aa6ySE6Ovrf/fddwEBAQkJCQ0NDXfu3Llz5w4hxMrKasWKFU5OTh0klerr60+fPi2VSl1dXT09PTkczrJly/Ly8u7fv//999+PGjVKR0dHIpFwuVwl05HszcuK7Uw5aeVjnj59+vfff19dXZ2Xl2dra6u4V7eiooIW1G5vbhV1Oqs9/B3o0u3MnLTZuc3GTuekT8ntswZ429AlwwAAr4+GhoZr164lJiYKhcIhQ4Y4ODisWbPGyMjI3d2dEBISEiK31YbWrMvNza2rqzM0NLS2tl66dKlcETaBQLBt2zZtbe3AwMCqqqqQkJAHDx5UVVUZGRmNGzdu7dq1hoaGipEoP7KBgcGFCxcEAsH58+ezsrJcXV23bt1KO9TV1UVFRSUnJ5eXl9fV1enr65uami5YsMDJyamDfwh0LDs7mx4LIxKJdHV1zczMlixZMm3aNLkPyc3NzVFRUffu3SspKVFTUzM3N3d0dHRzc6NVFpngCSFXrlyR+2a9j2a105C6FAAAAICq9F8CUU9Pr6qqav369StXruzr5+Jyubq6umKxmH0iHhvNDXXpiMC+GJNNT09v796927dvf/jwYVZWVlZWFp/PLygo8PHxWb16NT12sE337t1raGjQ09PbsmUL/QjF4XB27dpVXFzM5/OPHTtGd/uOGzdOyU9s7e10poc2stNhSsZsYGBgY2OTn5+fnJxsa2tLl9qNHj2aLmkkrN3HiktQu62H71eXbmfmRCwWGxsby/Vscz47nRMAAAB4SwiFQi8vL3q6NCGkuro6NjY2OTn5q6++UuwslUrPnj0bHR3NtJSWlpaWlsbHx3t4eKxfv17xW2eBQODl5cV83/ny5cuXL18mJib6+/uz90l0Y+SysrIDBw7IbWsoLy/fu3cvcy45fYFCoTAzMzMxMXH//v3d2GwREBBw7do15qFIJBKJRJmZmYsWLfL09GTaq6urDx48WFRUxLTk5eXl5eXFxMT4+vrq6+u3N37fzaqSIXUjAADouWf8F5k5OSMtLCaOG6vqWAAGgP5LIA4bNqywsJCeK9cPTExMxGJxmxtUpVIp/Yg2bNgwlY8ph8fjzZgxgxYSKSgouHTpUkZGRkhIiJubW3tr7ujecFtbW/bXmDwe7+9///vf/va38vLy3377jRBCv8FWhlAoZPbVstHz/hRfoDIxT58+PT8/PykpaePGjcxeXWYELS0tmq0TCoVMnRxGcXGxQCDQ19e3sbFR8iVQPXy/lL996NChPB5PIpEIhUJra2u5zu3tku54TgAAAOBtIJPJfHx8ysrKuFzuihUrJk6cKJVKU1JSIiMjjx07ptg/KCiIpplmzJjh4uJibGzM5/MjIiIKCgpCQ0N1dHSWL1/O7t/a2urr6/vq1audO3fa29tLpdK4uLjg4OCGhoaAgID9+/d3e2SpVOrv719ZWWljYzN69GjmfGo/Pz+xWMzj8Tw8POzs7AYPHiwUCqOiotLT0xMTEx88eECP1VZeamoqzR46OjouWbLE0NCwoqIiIiKCrtdzcnKyt7en8Rw9erSoqIjD4bi7uzs4OGhqaj58+DA8PFwkEvn5+X399dftpeH6aFaVD6mrAQBAr4i+F7/d6+C2tWvOHfmHqmMBGAD6r4gKXVqVkZHBLrhB1dfX79mzZ8eOHQUFBb31dLREb3x8fGtrq9ylhIQEule6q6u9+mJMQkhzc/MXX3yxf//+9PR0druVldX27dsJITKZjGYJ20RLfCguczM3N9+zZw/92cjIaPLkyUrGU1tbm5aWJtdYXV2dlJRECKEVpbsaM00vlpSUJCcn0726cnVa6LC08LGcc+fOHTlyhF1vREk9fL+Uv53D4dAK0XQTN5tMJrt//36b43c6JwAAAPDGS01NffLkCSHE29t7w4YNdnZ29vb2W7du3blzp+KBxeXl5aGhoYSQdevWHThwYMaMGWPGjHFxcfH19aWfK65evVpbW8u+paGhobi42NfXd8GCBcbGxqamph9++OHixYsJIbm5uT0Zuaqq6vHjx3v27PH19fX09Jw4cSIhRCKRPHr0iBCyY8cODw8PW1vbESNGTJ061dvb29LSkhCSl5fXjSkihIwZM+bLL7+cPHnyiBEjHBwcvLy86GbhrKws2i0hISEnJ4cQ8sUXX3z00UcTJkywtbX98MMP9+7dSwjJyMhobx1D382qkiF1IwAAAID+138JRLq6qrKyMigoiB54R8lkssDAwMLCwtra2tGjR/fu05WVlbH3OxBCqqurr169StopdyuVSnt9zE5paGhUVlbm5OTcvHmTPTOEkMLCQvpDBwvlmE9jJSUlcpfo0YeEEJFI1F4aq02XL19mf0xpaWk5f/58U1MTl8uln426GrO5uTlNsZ09e5YQMnbsWCMjI/ZdNHeWlpYml5HMzc3Nzs4mhCiuTOxUD9+vLt2+bNkyQkhKSkpmZia7871799pLi3c6JwAAAPDGu379OiHE0dFR7oTi+fPn0894bHfu3JFKpRYWFh4eHux2Lpfr6emprq7+6tUrmr9jc3V1pd/UMhwdHcm/T6fpycizZ8+eO3cue1mfWCyePXu2i4vLtGnT5Mahn/O7kQij2z4sLCzU1P7zLxcNDY0dO3bs2LGDLj8khNy+fZsQMn78eJp0Y8ycOZOeMEM/Uirqu1lVMqTuBUAIkUgkYgWvXr1q82UCAAD0UP9tYba0tFy4cGFMTMz169f5fP6CBQuGDRtWVVUVExND929u2LCB/bGghyZPnjxhwoSsrKzLly+/ePHCyclJT0+P7gKgh5Vs3ryZ/XGH/vz48eOKigoNDY02qxV3dUzlTZkyJTIyMiEh4fTp03PmzBk6dGhdXV1WVlZ4eDghxM7OroOaIXZ2dnT/7LFjxz7//HOak2pubg4KCgoLCyOEaGtr19bWnj59Wskj9jgczrNnz/bt27d8+fKRI0eWlJTcvHmTfn26aNEi5oy/rsY8ffp0Pp9Pv0tX3Ks7f/78X3/9lc/nf/PNN3T/jpaWVk5OTnBwsEwmGz9+PP1au0t6+H516XZnZ+ewsLDi4uJDhw55eHhMmDBBJpOlpaVdv37d3Nyc5nYVf707nhMAAAB449GyTnPmzJFr53A4Li4uly5dYjfSLQuTJk1S/FChq6s7atSoJ0+e5Ofnz5w5k32J2VzM0NbWlmvp3sguLi5ynU1MTP72t78pvkyJRMJ8x9xV5ubmqampiYmJf/rTn2bPns3Uk5FLuT5//rzNkDgczuHDhxsaGtqrjNd3s6pkSN0LgBDy7Nmzp0+fyjW2+a8YAACAnuu/BCIh5OOPP66urk5JSUlNTaWbESgOh7Nq1ar58+f34nNxOJx9+/YdOnTo6dOncXFxcXFx7EtbtmyhXxIyLC0tMzIyHj16tGnTJhsbmxMnTvR8TOVt2LAhIyODz+ffunXr1q1b7Eumpqb79u3r4F4DA4MNGzacP3++uLh4165do0aN0tbWLigooF/wLl269IMPPti9e7dYLD5+/PiJEyc0NTU7Dmby5MmDBg1KSEigK+MY48aN27BhQ7djnjFjBt2dweFwnJyc5K5qamp6e3t/+eWXZWVlYWFhNPXJjLZ3795uJJd7+H516XY1NbV9+/Z5eXmJxeLg4ODg4GDabmZm5u3t/cknn/B4PMWZ73hOAAAA4M1WW1tbX19PCGGX3WAoNtLzYSIjIyMjI9sbU7EknampaaeRdG/kNus4UxKJ5NmzZwKBQCAQPHny5PHjx/T4l25wd3ePjY2tra09c+bMhQsXpk6dOn78eHt7e/bram5urqioIO282I5noI9mVfmQuhcAIWTUqFH0yB25ng8fPuw4tv5RLiprqJcP28DIeMiQdzq9+parKC9raKiXazQwMNIa8k4PrwIA9ES/JhA1NTW9vLzu379/+/btJ0+eSCQSU1PTkSNHfvDBB724eZmhq6vr6+sbHR199+5dPp8vkUj09PTs7OyWL1+u+HSrV6+m1dykUmkHO0m7NKbyeDzeqVOnoqOj4+PjRSKRWCzW19c3MTFxdnaeN2+ehoZGx7e///77gwYN+vHHHxsaGpgNs1paWlu2bJk3bx6Hw9m9e/c333xTVFT0ww8/7Nixo+PRuFzu559/HhMTEx0dzefzORyOpaXlnDlzFi1axK7j3NWYx4wZY2hoWF5ebm9v3+Y3wKampqdPn46IiEhISBAIBJqamsOHD3dwcHBzc+s06dmeHr5fXbrd0tLyu+++CwkJSUpKqqqq0tPTc3JyWrNmDT2ess2X3OmcAAAAwBuMqeHb5scAxUaRSEQIUVNTY38kk6OYp+tgI0sPR25zsVtpaenFixdTUlKam5uZRlNTU5lM1l5luY7p6+t/9913AQEBCQkJDQ0Nd+7coadOW1lZrVixwsnJicPhVFRU0EN1uvGBqo9mVfmQuhcAIYTH4zHrMRmKh3eris/hLyN/CpVrPO73/TL3VZ1efcv5Hf/qRmSYXOM3Pv+3eJlHD68CZ6R8xUtCyPdXg7+/GsxueRgVgbrMAIr6NYFICOFwOM7Ozp0WizA0NIyIiFBs19TUbLOdEMKs+WJTV1dfsmTJkiVLOg1MW1v7wIED7JZhw4a1+VxKjtne7YSQd999V/GSpqbm0qVLly5d2mmoijgczsKFC2fOnPn06dPi4uLW1lZTU9NJkyYxHykcHR3bC6a9AV1dXV1dXTvu1qWYm5ub6ZksHezVHTx48OrVq1evXt1ehw5m1c7Orifv1+jRo3tyO6Wrq7t169atW7eyG+nWpDY/OyozJwAAAPCmYj4eiMVi5pQYhmKJPD09vaqqqvXr169cubJ3I+neyIobREpLSz/77LOamhotLa05c+ZYW1uPGDHC3NxcW1vbz8+vewlEGt7evXu3b9/+8OHDrKysrKwsPp9fUFDg4+OzevXqdevWManMNlfqdTp4X8yq8iH13dsKAADQi/o7gQh955133rG3t2dOkn7dpKWl1dfXq6uryx0j/WaIjIx8+vSpnZ3dvHnz5C7RXSRtLmt9s+cEAAAAOjZ06FB6krVQKLS2ll8Xo5huGzZsWGFhYXvVhHuit0YODQ2tqakxNzf38fGROxOwpaWlh4PzeLwZM2bQT00FBQWXLl3KyMgICQlxc3MbMmSIrq6uWCwWCoWKlfeKi4sFAoG+vr6NjY3isH00q1paWkqG1Hdvq2r5+P/g4/9D966+5f7he/Yfvmf74io0F+SyH35/NWjHwa+3frj6//7xNbudy+2/YrMAAwj+w4D+IJPJfvnlF0LI5MmTldlHM+BIJJLY2Njg4GC5DSa1tbVRUVFE4Zxv8hbMCQAAAHSMw+HQ8nd0Ty6bTCa7f/++XCOthpeRkSGRSOQu1dfX79mzZ8eOHcxpNl3SWyPn5+cTQmbNmiWXPZTJZHl5ed0IrLm5+Ysvvti/f396ejq73crKavv27XRkeoYgrYkcHx+vOMi5c+eOHDmSnJzc5lP03awqGVLfBQAActTVuew/9NwANQ5Hrr17lVEB3nhIIELfys7OpvVYHj16RAhRci/wgOPk5DRo0CChUHj06NHnz5/LZLKGhoaMjIxPP/20rq5OX1+fXSPlLZkTAAAA6NSyZcsIISkpKZmZmez2e/fuKeaM6JknlZWVQUFB9Hw9SiaTBQYGFhYW1tbWdu9U7t4aefDgweTfh/qxBQcH0wWVXT2hT0NDo7KyMicn5+bNm+zACCFMWedhw4YRQugRSWlpaXKpxtzc3OzsbEKI4jJAqu9mVcmQ+i4AAACAXoQtzNC3EhISmIpyU6ZMeW13WPeQmZnZrl27Tp48mZaWlpaWxuVymc/Hmpqan376KfuI67dkTgBef5cvX25vQYpKHD16dMyYMaqOAgD6lbOzc1hYWHFx8aFDhzw8PCZMmCCTydLS0q5fv25ubl5SUkJYRw1aWlouXLgwJibm+vXrfD5/wYIFw4YNq6qqiomJSUhIIIRs2LBB8VxCZfTWyO+++25ubu7t27f19PQcHBy4XG5JScnNmzezs7M5HA59aenp6ba2tlpaWkrGNmXKlMjIyISEhNOnT8+ZM2fo0KF1dXVZWVnh4eGEEDs7O7qTY/78+b/++iufz//mm29WrFgxceJELS2tnJyc4OBgmUw2fvz4iRMn9ulrV6RkSH0XAAAAQC9CAhH61vDhw/X09FpbW52dnTdu3PgGrwZ3dna2trYOCQl58uRJaWmpurq6gYGBvb29u7u7qakpu+fbMycAr6dBgwaNHTs2Nzc3MzNTbsmPai1dupTL5Y4cOVLVgQBA/1FTU9u3b5+Xl5dYLA4ODmaqApqZmXl7e3/yySc8Hk9TU5Pp//HHH1dXV6ekpKSmpqampjLtHA5n1apV8+fP73YkvTLyqlWrEhISysvLQ0NDQ0P/U2N36tSps2bNOnHiRGVl5aFDh/7xj38o/wXqhg0bMjIy+Hz+rVu3bt26xb5kamq6b98++rOmpqa3t/eXX35ZVlYWFhYWFhbG7rZ3794OcnB9NKvKh9R3bysAAEBvQQIR/mPjxo0bN27s3TEXLVq0aNGi3h3ztWVqarpr165Ou71VcwLwGmpsbMzJyVF1FG34y1/+snHjxoCAAFUHAgD9ytLS8rvvvgsJCUlKSqqqqtLT03NyclqzZg2twsxUaqY0NTW9vLzu379/+/btJ0+eSCQSU1PTkSNHfvDBBz3c5dorI9NqyyEhIQ8fPhQKhTwez8bGZuHChdOmTSOEPH/+/JdffrG0tDQwMFA+MB6Pd+rUqejo6Pj4eJFIJBaL9fX1TUxMnJ2d582bp6GhwfQ0NTU9ffp0REREQkKCQCDQ1NQcPny4g4ODm5sbOwnbR6+9TUqG1HcBAAAA9BaO3GEiAPB6ampqiomJcXBwMDMzU3UsAAPb+fPnP/74Y0LInj171q9fr+pwCCHk8ePHH330ESEECUToC62trb/99hshZMKECZaWlqoOB5T1xx9/HDhwwNbW1sfHR9WxwIBRWVmZmJg43GbSIB5q9HWfgc6g6rpmcX1T512hfepqahNNOKSqQtWBDGwxOU+aWlptbGzarCMP0J+wAhEAAN5SFhYWkydPVnUUhBDS0tKi6hAAQDUiIyOfPn1qZ2c3b948uUsPHz4khBgZGakiLgAAAID/guN4AQAAAABUQyKRxMbGBgcHNzX911qn2traqKgoQoiDg4OKQgMAAAD4DyQQAQAAAABUw8nJadCgQUKh8OjRo8+fP5fJZA0NDRkZGZ9++mldXZ2+vr6Tk5OqYwQAAADAFmYAAAAAABUxMzPbtWvXyZMn09LS0tLSuFxua2srvaSpqfnpp5/yeDzVRggAAABAkECE/lFYWLhnzx5CSFhYWMdV8HpRXFzcpUuXCCGbN29+7733+udJAQAAALrE2dnZ2to6JCTkyZMnpaWl6urqBgYG9vb27u7upqamqo4OAAAAgBAkEOENJpFIKioqCCGNjY2qjgUAAACgXaamprt27VJ1FAAAAADtQgIRAAAAAADgzTHceIi2to6qoxjAuByOgS5PJlN1HG8ANQ7R0VN1EANcXiEhraoOAoAQJBDhDbZ48eLFixerOgoAAAAAgH5VW9/cSpo67wft0BqkrsYlLa3IIPaU9mANVYcAAL0GVZgBAAAAAAAAAACgXViB2N8aGhquXbuWmJgoFAqHDBni4OCwZs0aIyMjd3d3QkhISIhcrb309PQbN27k5ubW1dUZGhpaW1svXbrU1taW3UcgEGzbtk1bWzswMLCqqiokJOTBgwdVVVVGRkbjxo1bu3atoaGhYiTKj2xgYHDhwgWBQHD+/PmsrCxXV9etW7fSDnV1dVFRUcnJyeXl5XV1dfr6+qampgsWLHBycuJyud2bouzs7J9//vn58+cikUhXV9fMzGzJkiXTpk3jcDjsbs3NzVFRUffu3SspKVFTUzM3N3d0dHRzcxs0aBA7eELIlStXdHT+axNHH81qpyF1KQAAAAAAAAAAgNcBEoj9SigUenl5lZWV0YfV1dWxsbHJyclfffWVYmepVHr27Nno6GimpbS0tLS0ND4+3sPDY/369XIJNUKIQCDw8vIqLy+nD1++fPny5cvExER/f38TE5OejFxWVnbgwIHKykp2Y3l5+d69e8ViMfsFCoXCzMzMxMTE/fv3K47TqYCAgGvXrjEPRSKRSCTKzMxctGiRp6cn015dXX3w4MGioiKmJS8vLy8vLyYmxtfXV19fv73x+25WlQypGwEAAAAAAAAAAKgQEoj9RyaT+fj4lJWVcbncFStWTJw4USqVpqSkREZGHjt2TLF/UFAQTTPNmDHDxcXF2NiYz+dHREQUFBSEhobq6OgsX76c3b+1tdXX1/fVq1c7d+60t7eXSqVxcXHBwcENDQ0BAQH79+/v9shSqdTf37+ystLGxmb06NFTpkyh7X5+fmKxmMfjeXh42NnZDR48WCgURkVFpaenJyYmPnjwYOrUrqtFEgAAIABJREFUqV2aotTUVJo9dHR0XLJkiaGhYUVFRUREBF2v5+TkZG9vT+M5evRoUVERh8Nxd3d3cHDQ1NR8+PBheHi4SCTy8/P7+uuv20vD9dGsKh9SVwMAAAAAAAAAAFAtJBD7T2pq6pMnTwgh3t7eDg4OtNHe3n7kyJGnT5+W61xeXh4aGkoIWbdu3erVq2njmDFjnJ2dfXx8fv/996tXr86dO1dbW5u5paGhobi42N/f38LCgrZ8+OGHYrE4KioqNze3JyNXVVVVV1fv2bNnzpw5TGJOIpE8evSIELJjx45Zs2bRxhEjRkyePHn37t3Pnz/Py8vrRgKRBvPll1+qqanRAe3s7LZt21ZeXp6VlUUTiAkJCTk5OYSQL774YsaMGfReW1tbS0vL48ePZ2RkvHjxYvjw4Yrj992sKhlSNwLoC/v27cvIyOjTpwB4nZWUlKg6BAAAAAAAgIEECcT+c/36dUKIo6Mjkz2k5s+fT4/8YzfeuXNHKpVaWFh4eHiw27lcrqen54MHD169evXo0aOZM2eyr7q6ujJ5LsrR0TEqKqqqqqqHI8+ePXvu3LnsFrFYPHv2bELItGnT5MYZPXr08+fPa2trO5kRBXRzt4WFBc0eUhoaGjt27CgvLzc1NaUtt2/fJoSMHz+eSdVRM2fONDY2Lisry87ObjOB2HezqmRI3Qug16Wnp8fGxvbpUwC8/ubOnTtixAhVRwEAb6+4uLhLly4RQjZv3vzee++pOpzeJxKJgoKC8vLyJBLJmDFjVq1aZWVl1f9hnD9/PjIy0tnZed++ff3wdG/82woAAG8tJBD7z7Nnzwghc+bMkWvncDguLi70owajuLiYEDJp0iR2Ko3S1dUdNWrUkydP8vPz5TJNzOZihuJatu6N7OLiItfZxMTkb3/7m+LLlEgkhYWFiu3KMDc3T01NTUxM/NOf/jR79mymnoxcypUmWxVD4nA4hw8fbmhoGDp0aJvj992sKhlS9wIAgL4QFRWlWOAIAKDfSCSSiooKQkhjY6OqY+l92dnZhw4dkkgk9KFIJEpJSfnss8/esJyat7d3ZmYme2fJm/22AgDA2wwJxH5SW1tbX19PCGGX3WAoNgoEAkJIZGRkZGRke2PW1dXJtTBr9DrQvZHbrONMSSSSZ8+eCQQCgUDw5MmTx48fNzU1dRpGm9zd3WNjY2tra8+cOXPhwoWpU6eOHz/e3t6e/bqam5vpx7I2X2zHM9BHs6p8SN0LoNdduXLl1atXff0sAK85TU1NVYcAAPBmamxsPHXqlEQimT9/Pi0Qd/Xq1ejo6HPnzjk4OGhpaak6QAAAAOgyJBD7CVPDt83FcYqNIpGIEKKmpsblctsbUzFPN2TIkE4j6d7IOjo6it1KS0svXryYkpLS3NzMNJqamspkMqFQ2GkkivT19b/77ruAgICEhISGhoY7d+7cuXOHEGJlZbVixQonJycOh1NRUSGTyUg7M9mxPppV5UPqXgC9btiwYX39FAAAANCxxYsXL168WNVR9In8/HyhUKirq+vp6amurk4I+eSTTzIzM0tLS/Pz8ydOnKjqAPvQG/y2AgDAWw4JxH7CpJbEYrGxsbHc1ZqaGrkWPT29qqqq9evXr1y5sncj6d7IiltuS0tLP/vss5qaGi0trTlz5lhbW48YMcLc3FxbW9vPz697CUQa3t69e7dv3/7w4cOsrKysrCw+n19QUODj47N69ep169YxqcxurNTro1lVPqS+e1sBAAAAXhO0VpWJiQnNHhJCuFzu8OHDS0tLX758+WYnEAEAAN5USCD2k6FDh/J4PIlEIhQKra2t5a4qptuGDRtWWFj44sWLXo+kt0YODQ2tqakxNzf38fGROxOwpaWlh4PzeLwZM2bQgiQFBQWXLl3KyMgICQlxc3MbMmSIrq6uWCwWCoXjx4+Xu7G4uFggEOjr69vY2CgO20ezqqWlpWRIffe2AgAAvIYEAsG2bdu0tbUDAwOrqqpCQkIePHhQVVVlZGQ0bty4tWvXtnlGSnp6+o0bN3Jzc+vq6gwNDa2trZcuXWpra8vuk5mZ6e3tzePxQkJC5G6/c+fOyZMnTU1Nf/jhB3YYBgYGFy5cEAgE58+fz8rKcnV13bp1K+3Q2toaHR199+5dPp8vkUj09PTs7OyWLVs2evRoxfCys7Np+TuRSKSrq2tmZrZkyZJp06ZxOBx2t+bm5qioqHv37pWUlKipqZmbmzs6Orq5uTGnr9KoCCFXrlyR2+qhzAx0Y247DalLAXSAlqgqKipqbm7W0NCgT03PyNbT01NykN591wghMpns5s2b0dHRfD6fEGJhYTF79uw///nPNEI2ZUb28/NjStIFBgYGBgba2NicOHGivbdVyclX8rcLAACg/yGB2E84HM7w4cOfPHly584dudOjZTLZ/fv35fqbm5sTQjIyMiQSCVNLhKqvr/fy8mppadmzZ083itn11sj5+fmEkFmzZsllD2UyWV5eXlejIoQ0Nzd7e3vLZLLVq1ezq6ZYWVlt375927ZtMplMIBBYWVlZWFiIxeL4+Hi5wtCEkHPnzv3xxx8eHh5tJhD7blaVDKnvAgAAAHidCQQCLy8v5kSXly9fvnz5MjEx0d/fn30StFQqPXv2bHR0NNNSWlpaWloaHx/v4eFBT9PrSRhlZWUHDhyorKxkN9bU1Bw6dKigoIBpEYlEsbGxcXFxmzZtcnNzY3cOCAi4du0au6dIJMrMzFy0aJGnpyfTXl1dffDgwaKiIqYlLy8vLy8vJibG19dXX1+/vQi7MQNKzq2SIfXKW2BlZaWjo1NTU3PlypVNmzY1NTX5+/tXVFRoa2t3Y/lhz981QohMJvP392dSfoSQgoKCgoKCO3fufP311+xMn5Ijv/POOwYGBmKxuKWlZfDgwfTr5PZegpKTr+RvFwAAgEoggdh/li1b9r//+78pKSmZmZn29vZM+71799ifUahZs2aFhIRUVlYGBQV99NFHzGc1mUwWGBhYWFiop6fX3verHeutkQcPHkz+fagfW3BwMF1Q2dra2qXANDQ0KisrS0tLb968OWnSJPbHU6asMz28z9nZ+Y8//khLS0tPT2enGnNzc7OzswkhissAqb6bVSVD6rsAAODNEBMTs2DBgp6PM27cOH9//56PA9ArWltbfX19X716tXPnTnt7e6lUGhcXFxwc3NDQEBAQsH//fqZnUFAQTV3NmDHDxcXF2NiYz+dHREQUFBSEhobq6OgsX76822FIpVJ/f//KykobG5vRo0dPmTKFECKTyXx9feknsdmzZzs5ORkYGBQUFISHh5eVlf34448WFhaOjo50hNTUVJrfcXR0XLJkiaGhYUVFRUREBF2v5+TkRD/gSaXSo0ePFhUVcTgcd3d3BwcHTU3Nhw8fhoeHi0QiPz+/r7/+ur00XFdnQMm5VT6kXnkLNDQ0tmzZcvLkyevXr48YMSIqKio/P5/L5e7evZt+gOzPd41KS/v/7N1rXFNXujDwFcIloAEDIkFuchGpDiJ4l3JRUexYbyBoK5wZj7EqBwWdeqvSsY7TqjAKg/6seqq2FfEA1rcw0gZBUAgiNyG2KIiUGCFAgBAucQOSvB/WO/vNSQA3CQja5/8prKys9ey190zjk3UpefnypY2Nzfr16x0cHF68ePHTTz/x+fxnz55dunQpMjISV6PeMofD4XA4+BTmgIAA8hTmfi+ByuBTfLoAAACA0QIJxDfHy8srJSVFIBAcOXIkKCho5syZCoWipKTk5s2bVlZWeLMYcqtBW1vb5cuXZ2Rk3Lx5UygULlu2zNLSUiKRZGRk8Hg8hFBoaKj6voRUDFfL06ZNe/LkSVZWFovF8vDwoNPpdXV1t2/frqiooNFo+NJKS0tdXFyon7U3d+7ctLQ0Ho8XHx+/ePHiCRMmdHZ28vn8GzduIIRcXV3xeSZ+fn63bt0SCoVHjx4NCAiYNWuWkZHR48ePr1+/rlAoZsyYMdCP2yM3qhRDGrkAAABvNTqdvnDhwkePHuET7bVvUCgUJicn+/n5UV8tCMDIkclkAoEgLi7O2toal3z00UdSqTQ9Pf3Jkydktebm5uTkZITQpk2byHSMo6Ojl5fXyZMn79+/f+3atSVLlqgsfaBOIpG0tbVFRkYuXryYzN+VlpaWl5fjToODg3H51KlTFy1adPDgwRcvXly+fHn27Nm4vLi4GId0+PBh/N9rOzs7V1fXbdu2NTc38/l8nOLh8XiPHz9GCB04cADvx4IQcnFxsbW1PXHiRFlZ2YsXL2xsbNQj1GAEKI4txZCG8Rb4+PjcvXu3pKQkNjYWIWRqarp7924NUmDa3zXs5cuX9vb2f//738ePH48QsrOzW7BgQVxcXE5OTnZ2dmBgIL4jGrT8WhQHn+LTBQAAAIwWSCC+OTo6Onv37j106JBUKr1+/fr169dx+eTJk6OiorZv385gMPT19cn6n3zySVtbW2FhYXFxMf5KgdFotODgYD8/P40jGZaWg4ODeTwe/qKJv2ti8+bN8/b2jomJaW1tPXLkyN/+9jfqX3dCQ0PLysqEQmFmZmZmZqbyW2w2e+/evfi1vr5+VFTU4cOHm5qaUlJSUlJSlKvt3r17kBzcCI0q9ZBG7rYCAN5efX199+/fH8YGKysrg4ODS0pKIIEIxgh/f38yw4XNmTMnPT1dIpGQJTk5OXK53NraOigoSLkmnU4PCwsrKip6+fLlo0ePFi1apHEYPj4+KpuN5ObmIoQmTpwYGBionBUyMTHZuHFjTEyMUCisra21t7dHCDU1NSGErK2tlb9p6OnphYeHNzc3s9lsXJKVlYUQmjFjBpktwhYtWjRp0qSmpqaKiop+E4iajQCVsaUY0jDegpaWlp6eHvyawWCcPn1a4/870vKukUJCQnD2kLyoLVu25Ofn9/T0cLlcDoejccuDozj4FJ8uZU1NTS0tLSqFCoWCemwAAAAAdZBAfKNsbW3PnDmTlJRUUFAgkUhYLJanp+fGjRvxKczkSc2Yvr7+oUOH8vLysrKynj59ShAEm82eMmVKYGCglqtch6VlfNpyUlLSw4cPGxsbGQyGs7Pz8uXL58+fjxB6/vz5v/71L1tbWzMzM+qB4e+XXC43NzdXLBZLpVJTU1MLCwsvL6+lS5cqb3HNZrPj4+NTU1N5PJ5IJNLX17exsfHw8Fi7dq1yEnaErr1fFEMauQAAAO+AgICA6OhoLRvZvXt3amrqsMQDwHDBK0+Vqc9iEwgECCF3d3f1HwJNTEzs7e2fPn1aVVWlTQLR19dXpQSfbObt7U2eF0zy9PT85z//2dPTU19fjxNGVlZWxcXF+fn5f/jDH3x8fMjtjJV3L0EIPX/+vN++aDTasWPHZDKZylc+kmYjQGVsKYY0XLegoKAgNjZWJpNZWFiIxWKCIBISEsLDw/G7fD6/u7t72rRpKkfHDETLu0bGr7KoGRfOnz8/Nze3vr5e45Zfi+LgU3y6lLW3t6tPWqe+9AcAAAAYEkggvmkmJiZbt24lz4/DfvvtN6SWQEQI0Wg0Ly8vLy+vwdu0tLQc6N+K06ZN6/ct7VtGCBkbG+Nfa9WFhISEhISQfzo4OFD816y+vv6qVatWrVr12pqGhoYbNmwYZMeZgYIfuVF9bUhDCgAA8DvEZDK1/y1BeYoNAGNEv/OnVOBUSFpaWlpa2kB1Ojs7tQlD/WBivHFzv+HR6XRzc/O6ujpcByG0bt26O3fudHR0nD179vLly/PmzZsxY4abm5vyx3t7e/GksH7bHHwcNBuB144t9ZCG5RbweLzo6Gi5XO7r6xsWFsblcr/55puMjIypU6f6+/srFIpjx44RBHHu3DmKCUQt7xpmYWHR77pjfMBdQ0ODxi0PjvrgU3m6VDg5OamfvNfa2kqeqAMAAAAMI0ggvjlpaWnPnj1zdXVdunSpylsPHz5ECJmbm49GXAAAAAAAIwvvYjw4fDKbjo4OnU4fqA65KlYzKhmrvr4+qVSK+vsRF5swYUJdXR15ZJypqemZM2euXLnC4/FkMllOTk5OTg5CyMnJKSAgwNPTk0ajtbS04DWkA7U5CM1G4LVjSz0k7W9BV1dXfHy8XC739/cPCwuj0WirV6+urKzMy8s7f/68vb29sbExQRB0Op1KThnT8q5hAy2gxo3gxUCatTw46oNP5emi3i8AAAAw7CCB+OYQBHHnzp1ff/3Vy8tLeU1rR0dHeno6GnSFAgAAAADAu43FYkkkkpCQkPXr12vTTldX10BvqazMpdPpJiYmUqm0ra2t3/o4naSce2KxWLt3796xY8fDhw/5fD6fzxcKhdXV1SdPntywYcOmTZvIbJcGkyWHawRUUA9J+wDu3bsnk8lYLBaHw8EJLxqNtmvXLoFAIBQKjx8/jhdcT58+fZAcpQrt7xpCaKDKeLKehYWFxi0PbkjPw2ufLur9AgAAAMMOznt9czw9PQ0MDBobG7/66qvnz58rFAqZTFZWVvaXv/yls7PT1NTU09NztGMEAAAAABgdlpaW6N+b0GmDXI5KBc4c9bsoVS6X43MtcGDKGAzGwoULt23bdvbs2VOnTs2aNQshlJSU1NXVZWRkZGJiMlCbAoGgoKCgqqqq32CGawRUUA9J+wDwImgXFxcDAwOykMFgfPbZZ4aGhs3NzT/99BNCaN26dRp3gTS6ayKRqN/TRerq6pDSUmLNnodBaPA8DPJ0Ue8XAAAAGHaQQHxzJk+evGvXLjqdXlJSEh4eHhAQsHHjxs8//7yhoUFfX/8vf/kLuVkyAAAAAMDvDd6NrqysjCAIlbe6uroiIyPDw8Orq6uVy7u7u1VW1Mrl8tLSUuqdTp48GSGUm5vb19en8haPx8ON48B6e3sPHDiwf/9+lfadnJx27NiBEFIoFDh9hs9Exuf5qvj666+//PLLBw8e9BuMBiNAEcWQtA8Anz2CVwQrs7KyioyMxK/Nzc1nz56twVWQqN81Unt7u/qwNzU1FRYWIoTIzWc1aPm1qAw+9acLAAAAGC2QQHyjvLy8zp075+fnZ2dnp6ura2BgMHny5A8++ODMmTOurq6jHR0AAAAAwKjx9vZGCLW2tiYmJipPFlMoFAkJCTU1NR0dHWSiB+8op1Ao8EbSpKysLKFQONROm5qafvjhB+Xytra2a9euIYSsrKzwkbt6enqtra2PHz++ffu2yly2mpoa/ALPTcPnpJWUlKgkg548eVJRUYEQmjFjhvYjMCQUQ9I+AFtbW4RQZWUlntmnDG99iBASi8V5eXkaXAWJ+l1T9v333yuvTe7p6Tl//nxfX5+RkdGKFSu0aVkulw8SLZXBp/50AQAAAKMF9kB809hs9q5du0Y7CgAAAACAscXW1nb58uUZGRk3b94UCoXLli2ztLSUSCQZGRk8Hg8hFBoaSm6HZ21tbWRkJJPJzpw5QxDEzJkzpVIpj8dLSkqaMmVKbW0txU5nz549c+ZMPp///fffv3jxwtPTk8ViVVdXJycn493xtmzZQh5eMXfu3LS0NB6PFx8fv3jx4gkTJnR2dvL5/Bs3biCEXF1d8Xkmfn5+t27dEgqFR48eDQgImDVrlpGR0ePHj69fv65QKGbMmIEXpWo5AkNCMSTtA3B1dWUwGARBHD9+fN++fTY2Ngih3t7exMTElJQUhBCTyezo6IiPj3dwcBjqVD7SkO4aNmHCBKFQuG/fvjVr1tjb2wuFQi6Xi2dTrlu3jjyIZqgt49e//PJLS0uLnp5ev+dKUxx8ik8XAAAAMFoggQgAAAAAAMaETz75pK2trbCwsLi4uLi4mCyn0WjBwcF+fn5kCZ1O3759+6lTp6RS6T/+8Q+y3MHBITw8fM+ePRR7pNFoe/fuPXLkyLNnz7Kzs7Ozs5Xf4nA4c+bMIUtCQ0PLysqEQmFmZmZmZqZyO2w2e+/evfi1vr5+VFTU4cOHm5qaUlJScOKMrLZ79+5BcnDUR2BIqIekZQBmZmahoaEXL14UCAS7du2yt7dnMpnV1dUdHR0IoVWrVgUGBkZEREil0hMnTsTEx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1euxAfLAAAAAKMCEohjF4vF2rp1K/mVkXTixAmVEmNjYw6H028jISEhISEhyiVMJvPgwYPatNAvGo22fPnyRYsWPXv2TCAQ9PX1sdlsd3d3BoOBK8yZMyc1NfW17VhaWqpUUy8hTZs2Tf2tfi8QR+jl5eXl5TXUADSOhHqnaOCwAQAAgLdXW1vb559/XltbS5ZUVlZWVlZmZGRER0ebmpriwvb29iNHjiifLSYWi+/cuZOdnb158+a1a9eqt1xcXPzll1++evUK/ykQCM6ePdvR0WFqahoXF0fuPlxTU3Pu3LmWlhaVLzMKheLs2bMZGRlkSVVVVVVVVW5u7ueff463hx6S8ePHm5mZSaXSV69eGRoaGhkZmZiY4Lfkcvm5c+eUfwBuaGhoaGjIzc0NCgoKCQlRT/81NTUdPHiwtbVVufDKlSs//PCD8hDhnx5XrFgRFhb22gidnJyMjY3b29uvXr26efPmnp6euLi4lpYWJpNJcfrhcF0jxacCq62tPXHiBF7UghASiUQikSg/P/+f//wnm81WiVAkEh06dKi5uRn/WV9fX19fn5+fHxcXZ2FhQVbT4HlTNqSPNzY2Hjp0qKmpibz2O3fuPHjw4K9//atyNbxhUU9PT3Fx8ZIlS5TfEgqFAoEA/Xt/bQAAAGC0QAIRDKfx48e7ubkNtNIZAAAAAL8fcrn8q6++qq2tpdFo69at8/Dw0NfXf/jw4Y0bN8RicWxs7BdffIF3W46OjsbpGB8fH09PTzMzs+rq6hs3bjQ1NV26dMna2nrOnDnKLRMEER0dbW9v/+c//9nW1raysvLs2bMSiSQhIUGhUEyePJnD4Tg5OdXW1p49e7ahoeGHH34ICgpSnt2WnZ3d2Ng4ceLEtWvXOjk51dfX3759+/Hjx7/++ut3332nwTwvDofD4XCioqLKy8sDAgI2bNhAvpWYmIgzawsXLvT19Z00aZJQKExNTa2urk5OTjY2Nl6zZo3KuMXFxbW2tjo7Ozs4OMydOxchVFxcjLOHc+bMWbly5cSJE1taWlJTU/GkP09Pz9d++9LT0+NwOKdOnbp586adnV16enpVVRWdTo+IiKCYMB2Wa6T4VJAt4x/OIyIiXF1d5XJ5RkZGSkoKQRDffvvt/v37lcPr6+uLjo5++fLlzp073dzc5HJ5dnb29evXZTLZlStXyMoaPG/KhvRxhUJx8uTJpqYmOp0eEBAwa9YsuVxeWFiYlpZ2/Phx5WYNDAw8PDwKCgoePHigkkDEe2ebm5v3O5cTAAAAeGMggQgAAAAAAIYfj8d7/PgxQujAgQMLFy7EhS4uLra2tidOnCgrK3vx4oWNjU1paWl5eTlCaNOmTcHBwTh5NHXq1EWLFh08ePDFixeXL1+ePXu2clKpt7d30qRJX331Fd5Ved68eT09PSdPnuzr65swYUJMTMy4ceMQQm5ubjt37jx06NCrV69EItGUKVPIFhobG9ls9ldffYV3aJk+fbqvr++pU6fy8vIyMjLWr18/+M4t1OE9pvHVkRk3R0dHLy+vkydP3r9//9q1a0uWLGEymeRHJBJJW1tbZGTk4sWLyavGmyk7OjoePnwYL8e2s7NzdXXdtm1bc3Mzn8+n8vOtj4/P3bt3S0pKYmNjEUKmpqa7d+/W/nffIV0jxaeCbLy7u/uf//zn5MmT8Z//8R//0dbWlpmZWVVVpRKGTCYTCARxcXHW1ta45KOPPpJKpenp6U+ePCGrafC8KRvSx4uLi58+fYoQioqK8vDwwC24ublNmTIlPj5epeUFCxYUFBSUlpb29PQobxeOE4heXl7qIREE0d3drVL48uXLfiMHAAAAtDTMJ88CAAAAAACAEMrKykIIzZgxg8wTYYsWLcIb2FVUVCCEcnNzEUITJ04MDAxUTpGYmJhs3LgRISQUCpWXu2JBQUHKSZYZM2bgF+vWrcPZQ2z69Ok43SaVSlVa+Pjjj5WzhLq6ulu3btXT0+vt7f355581vGY1OTk5crnc2to6KChIuZxOp+PTkF++fPno0SOVT/n4+CxZskR5NPAaWGtra3IzR4SQnp5eeHh4eHg4xSRgS0tLT08Pfs1gME6fPj0sq0aGdI0UnwqSv78/mT3EZs+ejRBSPlFQuTKZPcTwZECJREKWaPa8afbxmzdv4hjI7CHm5+dna2ur0vLcuXN1dHS6u7txghIbfP3yb7/9lqvm2bNnAwUPAAAAaAMSiAAAAAAAYPg9f/4cIeTr66tSTqPRjh07FhsbixfnvnjxAiHk7e1Nng5M8vT0xFnC+vp6lbccHR2V/ySThk5OTsrldDqd3I5ZGYPBUN+hmMVizZs3j4x8WODsj7u7u3LiDzMxMbG3t0cIqU+mUx80KysrhFB+fj6XyyUIgiz38PBYvnz5zJkzXxtJQUFBeHj4o0ePLCwsdHR0CIJISEgg3+Xz+UVFRe3t7dQvjTSka6T4VJBU/kQIqZ8xMkhl5amdmGbPm2Yf/+233xBCixcvVqlJo9HUR4DJZLq6uiKEHjx4QBbi6YdWVlZ4GFXY29t7qVH5nwYAAAAwXGAJMwAAAPC/SKVSKkeaAgAG0dvb29LSghBSP+lCpbCxsXGganQ63dzcvK6uDtdRpjzNkEq5CgsLC+XTlkk4T6fencZEIhFCKC0tLS0tbaA6nZ2dKiXqpwavW7fuzp07HR0dZ8+evXz58rx582bMmOHm5tbvuKnj8XjR0dFyudzX1zcsLIzL5X7zzTcZGRlTp0719/dXKBTHjh0jCOLcuXODpOcGQv0aqT8VJOXDT16Lymho9rxp8PGOjo6uri40wCX0W7hgwYLy8vLCwkKFQoH/M4QTiN7e3v3+V4nBYKjnx/v6+gYKfqRJW8UEIVMpNJkwkWE0Tst3ARh24qZGWVeXSuFE80njxo8flXgAeCtAAhEAAAD4fzw8PH799ddBFq+9LRwdHfft26e+RA6AN6alpQUfhTxhwoRBqvX19eHFxQNVmzBhQl1dnVgsHt7wBtriEJeTx/hqD0euo6PTb74SI5cVk9SzeKampmfOnLly5QqPx5PJZDk5OTk5OQghJyengIAAT0/PQX726Orqio+Pl8vl/v7+YWFhNBpt9erVlZWVeXl558+ft7e3NzY2JgiCTqdTTEdqfI0UnwplFDPCFCtr+bwN6ePkU9Rv5X4LFyxYcP78+ba2tsrKShcXl7fu/OVrF47nZ6WqFG4/EOO5dI2W7wIw7L784tD/+SFJpfD0mYtrAzf0Wx8AgCCBCAAAAJBKS0tFIhGdTp81a9Zox6KVmTNnfvLJJ6MdBfhdI1Ng6tPrlNHpdBMTE6lU2u+WdujfexeyWKzhDW+gtbp4szwq6a0utakr/WKxWBKJJCQkZP369dTDU18LjJvavXv3jh07Hj58yOfz+Xy+UCisrq4+efLkhg0bNm3aNFBr9+7dk8lkLBaLw+HgPCONRtu1a5dAIBAKhcePH8crf6dPnz5IBnAQ1K+R4lMxcrR83ob0cfIpkkqleHtHZf0+gWZmZs7OzlVVVQ8ePHBxccHTDx0cHPDEWAAAAGB0QQIRAAAA+F/Gjx+PDzwFAGjMyMgIp1oaGxvJE05IAoFAJBKZmpo6OztbWFjgauqNyOVyfHiIpaXl8IbX2NhIrhJVhnevo9JdQ0MDlY4sLS1ramrwxnnDgsFgLFy4EJ9AUl1d/d1335WVlSUlJa1du3ag+Xd4ibGLi4uBgYFyO5999tmePXuam5t/+uknhNC6des0C4n6NVJ/KjSLhAotnzfqH58wYQKDwSAIorGxcerUqSqVB1olvWDBgqqqqoKCgj/96U/k+mWq1zbadhz4x44D/xiJdwEYdqfP/vfps/892lEA8JaBQ1QAAAAAAMDww+fh4lNrVXz99ddffvklPiwCn7Gbm5urvncbj8fDS1+HfQZWR0dHSUmJSmFbW1tBQQEZOam7u1tllbFcLi8tLaXSEY68rKxM+eQTrKurKzIyMjw8vLq6evBGent7Dxw4sH//fpVOnZycduzYgRBSKBQ4S9gvfNyH+pQ3KyuryMhI/Nrc3ByfbqyBIV0jxadi5Gj5vFH/OI1Gs7GxQQjhxebKFApFXl5ev+3j1HBdXd2DBw/w+mX1034AAACAUQEJRAAAAAAAMPxw4qOkpEQl7fXkyZOKigqEEJ6DhidYNTU1/fDDD8rV2trarl27hgY+glZL33//fUdHB/nnq1evLl682NPTQ6fTP/jgA1yIV6EqFIqHDx8qfzYrK0soFA7UslwuJ1/jq2ttbU1MTMTb/2EKhSIhIaGmpqajo8PBwWHwUPX09FpbWx8/fnz79m3lRhBCNTU1+MUgk+bwdqiVlZV1dXUqb+GtDxFCYrF4oJRWvzS+RopPxcjR8nkb0sdXr16NECosLCwvL1eufO/evYGyxlZWVjjteO7cOYTQe++9Z25uPtRrBAAAAEYCLGEGAAAAAADDz8/P79atW0Kh8OjRowEBAbNmzTIyMnr8+PH169cVCsWMGTPwZqOzZ8+eOXMmn8///vvvX7x44enpyWKxqqurk5OT8TEUW7ZsGfaD0Wk02m+//bZ37941a9ZMmTKlrq7u9u3bjx8/RgitWLGC3LHO2trayMhIJpOdOXOGIIiZM2dKpVIej5eUlDRlyhT1A5dwnL/88ktLS4uenp6xsbGtre3y5cszMjJu3rwpFAqXLVtmaWkpkUgyMjLwAtXQ0NB+dzxUMXfu3LS0NB6PFx8fv3jx4gkTJnR2dvL5/Bs3biCEXF1dBzk/xNXVFa+lPX78+L59+3B+qre3NzExMSUlBSHEZDI7Ojri4+OpbLen5TVSfCpGjpbP25A+7uXllZKSIhAIjhw5EhQUNHPmTIVCUVJScvPmTSsrK5zPVb/7CxYsEAqFra2t6K1avwwAAOCdBwnEd5ZYLE5MTKysrCQIwtHRMTg42MnJabSD6odIJNq2bRtC6OrVq+pnDr4xNTU1eBVPSkqKvr7+aIUBAAAAvDP09fWjoqIOHz7c1NSUkpKCc1UYm83evXs3Tp3QaLS9e/ceOXLk2bNn2dnZ2dnZZDUajcbhcObMmTPssc2ePdvAwIDH4+F5XqTp06eHhoaSf9Lp9O3bt586dUoqlf7jH/9/gzYHB4fw8PA9e/aoNGtra1tWVvbo0aPNmzc7OzvHxMQghD755JO2trbCwsLi4mLl/VVpNFpwcLCfnx+VgENDQ8vKyoRCYWZmZmZmpvJbbDZ77969g3zWzMwsNDT04sWLAoFg165d9vb2TCazuroaT8BctWpVYGBgRESEVCo9ceJETEzM4F+EtLxGik/FyNHyeRvSx3V0dPbu3Xvo0CGpVHr9+vXr16/j8smTJ0dFRW3fvp3BYKiP9sKFC5OTk3GDnp6e2l8yAAAAMCwggaihqKio8vLyTZs2bdgwFg96r6ioOHLkCLkTjVgsLiws/PTTT99//33qjYzxawQAAADAGMdms+Pj41NTU3k8nkgk0tfXt7Gx8fDwWLt2rXLexMTEJDo6msvl3r17VygUEgTBYrFcXV3XrFnz2uW9mqHT6fv27cvIyOByuUKhkEaj2draLl68eMWKFSonEfv6+pqbmyclJdXW1nZ1dZmbm3t7ewcGBra0tKg3u2HDBrFYXF5eLpfLyZWn+vr6hw4dysvLy8rKevr0KUEQbDZ7ypQpgYGB1K+OwWCcPn2ay+Xm5uaKxWKpVGpqamphYeHl5bV06VI9Pb3BP/7hhx8aGBhcunRJJpORi2eNjIw4HM7SpUtpNFpERMTRo0dra2svXLgQHh4+SFPaXyPFp2LkaPm8Denjtra2Z86cSUpKKigokEgkLBbL09Nz48aNeEvKfs/7dnR0nDhxYnNzs5ubG5UDwQEAAIA3g6aykQqgaCwn17q7u8PDwxsbG/38/EJCQmg02rVr17hcrrGx8YULF4yMjCi282auEWYgUtTT05ORkeHh4YF37wYAjITJk/8ve3ce19Sd74//EyIxouyLYRUVkMIgi4hVBsQV+6AqYFFb9VqnWKsPtDodR72I1Y4/UfBaHGXsTL+3WlsqwyK3UKkiohWDgoCAyi4CEUMIIYQlhiXJ749z59xMooCIxOX1fMwfyft8zue8z+mZFt58Fis+n29oaNje3q7tXLRg7ty5169fJ4QIBAJ6Cie8AeRyObXN7vTp06nl8N5m33//fWpq6qxZsyIjI7Wdy2jr6up68OBBQ0ODXC7ncDienp5sNlvbSb2l7t+/v2fPHmdn55iYGLVDvb2969ev7+7u3rZt2xAHqKpqa2vLy8vrHDtZzhg7eGt4ht9NNv6dvQl+U35x0p5+cWePtrN4vVWU5Mn7+5ycnF7qDvUAQ4ERiG+g6upqgUBgaGi4ZcsWat+9zz77rLS0tLm5ubq6+mWvLAMAAAAAr6AJEya4u7u7u7trO5G3RUZGxoMHD9zc3BYsWKB2iNqW56kbpBQVFXV3d48ZM4bakRkAAOAVgV2Y30DUkswTJ06kqoeEECaTSS2Y/fjxY21mBgAAAADwdpDJZDk5OYmJib29varxzs7OzMxMQoiXl5faKUql8pdffiGEzJgxY4CNcQAAAEbfSI5A7Ovry8zMvH79elNTk46OjrW1tbe3d3Bw8Nix/zZ+Xi6Xay4asmzZMrVFQ1pbW//whz/o6+snJCTU1tYmJiZWVFT09PRYWlq+//77ixcvZjAYFRUVycnJVVVVvb291tbWgYGBgYGB9NLL1BSVDz/8cPXq1b/99ltmZmZDQwODwbCxsZk/f35gYKDqGjelpaVRUVFsNjspKUntvq5du3bs2DEOh/OPf/yDEBIXF5eTk0MdSkhISEhIoBeQphQXF1+8eLGysrKrq8vMzMzR0XHp0qXOzs6qfVLzdk1NTU+fPs3n87/99tuysrLAwMCNGzdSDcrLy3/++efGxkahUGhoaGhlZRUUFDRr1qyh7EI4adIkQkh9fX1fXx+1Jk5fX19dXR0hxNjYeNDTR+oeKUN8Kyitra3nzp0rLi7u6OgwMzN75513PvroI9VpdNRzo94KsViclJR0+/ZtsVhsbm7u4uLy0UcfmZmZqfU5xPftWZ7rdKlUev78+by8PIFAMH78eC8vr9WrV5ubm4eEhBBCkpKS2Gz2rVu3Dh06xGAwfvjhB80p21u3bm1oaAgKCqKmdQMAAADAa8rX1zcpKUkgEERHR2/YsMHW1vbJkyfV1dV/+9vfurq6TExMVPdIKS8vF4vFpaWld+/eJYQEBQVpL3EAAICnGLECYnt7+759++rr6+lIVVVVVVVVVlZWbGysiYkJFezo6Ni/fz+9eDMhRCgU5uTkXL16dcOGDcHBwZo9FxYWHjp0qL+/n/ra0NAQHx/f2dlpYmJy/PhxemWKurq6U6dOiUSitWvXqp6uVCrj4+OzsrLoSHV1dXV1dW5u7r59+8aNG/e8dzphwgRTU1OJRNLf3z9u3Dg9PT1DQ0PqkEKhOHXq1KVLl+jGzc3Nzc3Nubm5YWFh1HKEar21tLTs2bOnra1NNXjmzJnz58+rPiJqseolS5Zs2bJl0AwdHBwMDAw6Ojp+/PHHDRs29Pb2Hj9+XCQS6evrD3H+8kjd4xDfCkp9ff2RI0eoDQEJIXw+n8/n5+Xl/fWvf+VwOGoZ8vn8yMjI1tZW6uvjx48fP36cl5d3/PjxiRMn0s2G8b6peq7TBQJBZGRkS0sLfe85OTn5+flffvmlajNPT08Wi9Xb21tYWDh//nzVQzwer6GhgRDi7+8/cGIAAC9Ve3v7oHsyaMv48eNfzZVqAQDUWFlZbdu27dixY0VFRUVFRUwmUy6XU4dYLNYXX3yhugYll8vNyMigPs+cORMzzQEA4FUzMgVEhUIRHR1dX1/PYDBCQkK8vLxYLNadO3dSU1OFQmFcXNyBAwcYDIZSqYyNjaXKMXPnzvX19TU1Na2trU1NTW1pafnuu+9sbGy8vb1Ve5bJZLGxsZMnT/7444/t7Oyqqqri4+PFYnFCQoJSqbSysgoPD3dwcKivr4+Pj29ubj5//nxYWJjq6LarV68KBAIzM7Pg4GAHB4fHjx9fvny5oqLi/v37Z8+eHcY4r/Dw8PDwcGqDkdDQUNUNRs6dO0dV1mbPnh0QEGBhYcHj8dLT02tra5OTkw0MDJYvX6723I4fP97W1ubk5DRlypSZM2cSQgoLC6nqobe3d1BQkJmZmUgkSk9Ppwb9+fr6DvrzhK6ubnh4+LFjx9LS0iZNmpSZmVldXc1kMj///PMhFkxH5B6H+FbQPR85coQQ8vnnn7u5uSkUiqysrJSUFJlM9v333+/atUs1PblcHhsb++TJk61bt7q7uysUiqtXryYmJkql0jNnztCNh/G+qXqu05VKZUxMTEtLC5PJDA0N9fDwUCgUBQUFGRkZhw8fVu127NixXl5et27dys/PVysgcrlcQoi5uflTx3ICAIyaadOmaTuFZ/ruu+82bNig7SwAAIbEz8/P0dExKSmppqamubl5zJgxpqam7u7uISEhan8gt7W1NTY2lsvlfn5+69evH8qsIwAAgNE0MgVELpdbUVFBCNm9eze93K+zs7Odnd2RI0dKSkoePXpka2tbXFxcWlpKCFmzZs3KlSup/y46OjrOmTNnz549jx49On369IwZM1T/e9nX12dhYREdHU0NN/Dx8ent7Y2JiZHL5UZGRkePHqUWB3F3d9+6dWtkZGR/fz+fz7e3t6d7EAgEHA4nOjra1NSUEOLi4hIQEHDs2LEbN25kZWV98MEHVPzFtba2JicnU3dHV9ymTp3q5+cXExNz8+bNn376af78+fr6+vQpYrG4vb19+/bt8+bNo++6sLCQOnHv3r3UdOxJkya5ublt2rSptbW1rKxsKH+QnDt37m+//VZUVBQXF0cIMTEx2bFjx4v/JfO57nGIbwXdeU9Pz1//+ld6i+H/+I//aG9vz87Orq6uVktDKpU2NDQcP37cxsaGinz44YcSiSQzM7OyspJuNoz3TdVznV5YWFhTU0MIiYqKopezcXd3t7e3P3HihFrP77777q1bt4qLi3t7e1XH0VAFRD8/v5f9I2NnZyc9pBcAVCkUCm2noE329vZ8Pp/6t9kr69SpU1VVVWp/mwEYuvXr169fv17bWcBbhMPhbNu2bdBmS5YsWbJkyUhdNMjNxGACllB8AbosokMINmF+YQbybgNFp7azeL1VE6Vc2zkAUEamgHjlyhVCiKurq9pmYXPmzLGwsGhpaSkvL7e1tc3NzSWEmJmZrVixQrVEYmhouHr16qNHj/J4vPr6+smTJ6t2EhYWplpkcXV1pT6EhISoLi3s4uKio6OjUCgkEolaeh999JFqlXDMmDEbN27Mz8/v6+u7ePHimjVrXvD2KdeuXVMoFDY2NmFhYapxJpO5ZcuW27dvP3ny5O7du3PmzFE9OnfuXLVhaNQcWBsbG3oxR0KIrq5uREREa2ur5mTepxKJRPRqzWw2++uvvx7i6ocDe657HOJbQccDAwPp6iFlxowZ2dnZ7e3tmpkEBgbS1UOKt7d3ZmamWCymI8N734Z3elpaGpWD2mLYCxcupNayVA3OnDlTR0enp6entLSUGnZKRnf+cnBwML3GJQAArb6+nqoeuru709twvToUCsWdO3du3779ltd5AQAGJ+0kyj5tJ/E6m2BAxugSJSqIL6y7U8mr03YSrzkF6ofwqhiZXw+o+khAQIBanMFgHDx4UCqVGhkZEUIePXpECPH399f8tcTX1/evf/1rb2/v48eP1Qo6U6dOVf1KFw0dHBxU40wmk81mZyY8QwAAIABJREFUS6VStZ7ZbLafn59a0NjY2MfHh8vlqlV2XgRV/fH09FQt/FEMDQ0nT55cU1NTXV2tVkDUfGjW1taFhYV5eXm/+93v5s6dS6+NorlN27PcunUrLi5OKpVOnDhRKBTKZLKEhISIiAjqaFlZWU9Pz7Rp0zR38BjUc93jEN8KGl1Kow2QoWZj1aGdlOG9b8M7/eHDh4SQefPmqbVkMBgBAQFnz55VS9XNza20tDQ/P5++EWr4obW19bPyAYDRERoaum7dOm1noWVZWVmqG1i9Irq7uydMmKDtLAAAAAAA3kYjUEDs6+sTiUSEkKcOjlMNCgSCZzVjMpnm5uZNTU1UG1WqwwyHElczceJE1d2WadbW1nRKI4LP5xNCMjIy6PWPNXV1dalFNHcNDgkJycnJ6ezsjI+PP336tI+Pj6urq7u7+xDHHnK53NjYWIVCERAQsGXLlkuXLv33f/93VlaWo6NjYGCgUqk8ePCgTCY7derUMAqIQ7/Hob8VNNXNTwY1lKcxvPdtGKd3dnZ2d3eTZ9zCU4PvvvtuaWlpQUGBUqmkhjdSBUR/f/9RWPLGy8tLswQMAJT3339/0B2WAAAAAAAA3iojUEAUiUTUVshqA8rUyOVyanLxs5oZGRk1NTUJhcIXT0nVs5Y4pOL0Nr4vjspcR0fnqfVKCj2tmKZZxTMxMTl58uSZM2e4XK5UKr127dq1a9cIIQ4ODqGhob6+vgMUmLq7u0+cOKFQKAIDA7ds2cJgMJYtW1ZVVXXjxo2///3vkydPNjAwkMlkTCZziOXIYd/jEN8KVUOsCA+x8Qu+b891Ov0WPbXxU4Pvvvvu3//+9/b29qqqKmdn51Hefzk2NnYUrgIAAAAAAAAAb4YRKCDSJTDN4XWqmEymoaGhRCJ56pJ2hBCqXjMiS/Wp6ujoeGqcWixvKOUtanDZoIyNjcVi8dq1az/44IOhp/fUgWDGxsY7duzYvHnznTt3ysrKysrKeDxebW1tTEzMqlWrBli08fr161Kp1NjYODw8nKozMhiMbdu2NTQ08Hi8w4cPUxNmXVxcBqgADmDo9zjEt+LlecH37blOp98iiUSiOenvqW+gqampk5NTdXV1fn6+s7MzNfxwypQp1MBYAAAAAAAAAIBXxwhMY9TT0zM0NCTPmA7c0NBw69YtaiNdai7nU5spFApq8xBLS8sXT0mVQCBQPm3528ePHw/xcs3NzUO5ENUVtXDeiGCz2bNnz960aVN8fPyxY8c8PDwIIUlJSQMUNKkpxs7OzmPHjlXt5z//8z/HjRvX2tr666+/EkJCQkKGl9LQ73Hob8XL84Lv29BPNzIyopaqfGrjZ82Sfvfddwkht27dIirzlwe7JwAAAAAAAACA0TYy66BR++FSu9aq+eabbw4dOpSfn08IofbYzc3NlcvVNxLicrnU1NcRH4HV2dlZVFSkFmxvb6cKN2o7+fb09KjNMlYoFMXFxUO5EJV5SUmJTCZTO9Td3b19+/aIiIja2tqBO+nr69u9e/euXbvULurg4LB582ZCiFKppKqET0Vt96E55M3a2nr79u3UZ3Nz8xkzZgzljjQ91z0O8a14eV7wfRv66QwGg9pOmppsrkqpVN64ceOp/VObUzc1NeXn51PzlzV3+wEAAAAArZPL5cuWLVu2bBn9g+7Vq1c3bNiwYcOGZ/2kNyLq6uqo62qugwQAADDKRqaASBU+ioqK1MpelZWV5eXlhBBXV1fyrwFWLS0t58+fV23W3t7+008/kZe2Be0PP/zQ2dlJf+3v7//22297e3uZTOZ7771HBalZqEql8s6dO6rnXrlyhcfjPatnhUJBf6burq2t7dy5c6pjHpVKZUJCQl1dXWdn55QpUwZOVVdXt62traKi4vLly2oDJ+vq6qgPAwyas7OzI4RUVVU1NTWpHaKWPiSECIXC5/pBZ9j3OMS34uV5wfftuU5ftmwZIaSgoKC0tFS18fXr159VNba2tqbKjqdOnSKEvPPOO+bm5s97jwAAAAAw+mQymUgkEolEPT092s4FAABgNIzAGoiEkIULF164cIHH43311VehoaEeHh56enoVFRWJiYlKpdLV1ZWafjtjxozp06eXlZX98MMPjx498vX1NTY2rq2tTU5Oprah+OSTT0Z8C1oGg/Hw4cOdO3cuX77c3t6+qanp8uXLFRUVhJAlS5bQK9bZ2Njo6elJpdKTJ0/KZLLp06dLJBIul5uUlGRvb19fX6/ZLSHk3r17IpFIV1fXwMDAzs5u8eLFWVlZaWlpPB5v0aJFlpaWYrE4KyuLmqC6bt26oWx9O3PmzIyMDC6Xe+LEiXnz5hkZGXV1dZWVlaWmphJC3NzcBtg/xM3Njc1my2Syw4cP//nPf6bqU319fefOnUtJSSGE6Ovrd3Z2njhxYijL7b3gPQ7xrXh5XvB9e67T/fz8UlJSGhoa9u/fHxYWNn36dKVSWVRUlJaWZm1tTdVzNf/pv/vuuzwer62tjWD+MgAAAAAAAAC8qkamgMhisaKiovbu3dvS0pKSkkLVqigcDmfHjh1U6YTBYOzcuXP//v0PHjy4evXq1atX6WYMBiM8PNzb23tE8lE1Y8aMsWPHcrlcapwXzcXFZd26dfRXJpP52WefHTt2TCKR/Nd//RcdnzJlSkRExB//+Ee1bu3s7EpKSu7evbthwwYnJ6ejR48SQj799NP29vaCgoLCwsLCwkLVu1u5cuXChQuHkvC6detKSkp4PF52dnZ2drbqIQ6Hs3PnzgHONTU1Xbdu3bffftvQ0LBt27bJkyfr6+vX1tZSAzCXLl26YsWKzz//XCKRHDly5OjRoywWa4DeXvAeh/hWvDwv+L491+k6Ojo7d+6MjIyUSCSJiYmJiYlU3MrKKioq6rPPPmOz2ZpPe/bs2cnJyVSHvr6+L37LAAAAADAK3nvvPXomEwAAwNtgZAqIhBAOh3PixIn09HQul8vn81kslq2trZeXV3BwsGrdxNDQMDY29tKlS7/99huPx5PJZMbGxm5ubsuXLx90eu/wMJnMP//5z1lZWZcuXeLxeAwGw87Obt68eUuWLFHbiTggIMDc3DwpKam+vr67u9vc3Nzf33/FihUikUiz21WrVgmFwtLSUoVCQc88ZbFYkZGRN27cuHLlSk1NjUwm43A49vb2K1asGPrdsdnsr7/++tKlS7m5uUKhUCKRmJiYTJw40c/Pb8GCBbq6ugOf/v77748dO/a7776TSqX05Fk9Pb3w8PAFCxYwGIzPP//8q6++qq+v/8c//hERETFAVy9+j0N8K16eF3zfnut0Ozu7kydPJiUl3bp1SywWGxsb+/r6rl69mlqS8qn7fU+dOtXMzKy1tdXd3X0oG4IDAAAAAAAAAIw+xlN3KH4zfP/996mpqbNmzYqMjNR2LqOtq6vrwYMHDQ0Ncrmcw+F4enpS2wTD6Lt///6ePXucnZ1jYmLUDvX29q5fv767u3vbtm2DDlDt7e3Nysry8vKidncBABhxc+fOvX79OiFEIBDQS3y8Orq7uydMmEAImTFjhuoQeBiUXC7/9ddfCSHTp0+nlksGeC20trb+4Q9/0NfXT0hIqK2tTUxMrKio6OnpsbS0fP/99xcvXsxgMCoqKpKTk6uqqnp7e62trQMDAwMDAzWnuRQXF1+8eLGysrKrq8vMzMzR0XHp0qXOzs6aF21ra0tNTb19+3Zra+v48eNdXFxWrFgxderUkJAQQsixY8ccHBwIIXw+f9OmTYSQH3/80cDAgD69r68vMzPz+vXrTU1NOjo61tbW3t7ewcHBY8eOVb1KV1dXZmZmfn5+a2trV1eXiYkJh8NZtGiRr6+v6hCHuro6aiPElJSUIf71va2tLS8vz9/FyWAcfvh/ARMMiJ4+eXN/Ux41SuFjZWOdtrN4vWWLunsVSicnJycnJ23nAm+7ERuBCK+UCRMmuLu7u7u7azuRt0VGRsaDBw/c3NwWLFigdojaluepG6QUFRV1d3ePGTOG2pEZAAAAADQVFhYeOnSov7+f+trQ0BAfH9/Z2WliYnL8+HF6PERdXd2pU6dEItHatWvpcxUKxalTpy5dukRHmpubm5ubc3Nzw8LC1q5dq7oidkVFxYEDB6RSKfVVIpHcvHkzPz9/69atQ8mzvb193759qounV1VVVVVVZWVlxcbGmpiYUMHW1tYdO3ZIJBK6mUAgEAgEpaWleXl5u3btGvFF4QHeBg8f88tqHkyy5Hg4ObxSpwC8MVBABBgBMpksJyfn/v37fn5+qn8f7uzszMzMJIR4eXmpnaJUKn/55RdCyIwZMwbYGAcAAADgbSaTyWJjYydPnvzxxx/b2dlVVVXFx8eLxeKEhASlUmllZRUeHu7g4FBfXx8fH9/c3Hz+/PmwsDB6xN+5c+eo6uHs2bMDAgIsLCx4PF56ejq1M56BgcHy5cvpCx0+fJiqHgYFBXl6eo4ZM6akpOTnn38+efLkoHkqFIro6Oj6+noGgxESEuLl5cVise7cuZOamioUCuPi4g4cOEBVBuPi4iQSCZvNDgsLc3NzGzdunEAgyMzMLC4uzsvLu337to+Pz8t6mgBvrqxbt7fEfP1pyNJTu9Q3MNDuKQBvDBQQAUaAr69vUlKSQCCIjo7esGGDra3tkydPqqur//a3v1HTUlT3SCkvLxeLxaWlpXfv3iWEBAUFaS9xAAAAgFdaX1+fhYVFdHQ09TdaHx+f3t7emJgYuVxuZGR09OhR6g+x7u7uW7dujYyM7O/v5/P59vb2hJDW1lZqw7o1a9asWrWK6nDq1Kl+fn4xMTE3b9786aef5s+fr6+vTwi5cOGCWCwmhOzevXvOnDlUYy8vLzc3t4MHDw6aJ5fLraiooE6nJ5c4Ozvb2dkdOXKkpKTk0aNHtra2MpmM+gkwIiLC39+fajZp0qQZM2Z8/vnnjY2NVVVVKCACAMArCAVEgBFgZWW1bdu2Y8eOFRUVFRUVMZlMuVxOHWKxWF988YXqGpRcLjcjI4P6PHPmTMw0BwAAABhAWFiY6gwPV1dX6kNISIjqNA4XFxcdHR2FQkHPDr527ZpCobCxsQkLC1PtkMlkbtmy5fbt20+ePLl79y5VLkxPTyeE+Pj40NVDysyZM2fNmnXr1q2Bk7xy5QqVm9rSNHPmzLGwsGhpaSkvL7e1tZVIJHPnziWEzJo1Sy2lKVOmNDY2dnZ2DuWZE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lIS3ic6Ojo6OtrKyio0NFTCgAEAAAAApNXU1NTtOM6C6evrI4TIZLKmpiaTyexpZ5wpwzdjxAS6hoYG0fmG3R5BR0fHysqqqKgoKyvL2toaTz80NzfHE/rE+Ouvv1gsFoVCCQgIwHdEJBIpKCiovLycTqcfOHDAyckJIWRjY9PthEEhFApl06ZNa9euffr0aX5+fn5+Pp1OLykpOXTo0MKFC5cuXUrs2dOCaLx2G2cGpbpoggRrGvZXwAAAAMDAgwTiIMbn88PDw4kUFUKopKSkpKQkJSXlp59+EkzqNTc37927t6SkhBhhMBhJSUnJyckrV66cM2eO4GFfvnz5008/ESUImUzm48ePs7KyNmzYILibvb29oqIih8PJycmZNm2a4CbJnzNbWlpqaGg0NzdfunRp5cqVHA4nPDy8vr5eXV193Lhxkl+Kc+fO3bx5U/DdMRgMGo3m5eW1bt06wT35fH5kZGRCQgIxUlRUVFRUlJqa+sMPP6ioqBDjUl00rLa2dteuXQ0NDfhXNTU1HR0dJpPZ1dWloqKiqqqqqakpbcAAAAAAAJKrqqri8/mizyPxsg9i4p6+vj6TyRRdb4sQ4vF4uKHH0KFDEUJffPEFHq+srBRNIBILnIU4OzsXFRVlZmauWLGCWL8sSfAIIWtrayUlJWJQWVl59+7dmzdvrquru3fvHkLIz8+v10MJvtzFxcXFxQUhVFJScuHChWfPnsXExMyZM2fIkCF4n5qamm4vGl4Qg68DkuaivQ9JAgYAAAAGHixhHsRyc3OTkpKMjY03bdoUERGxY8cOXF+5tLT0zJkzxG58Pv/w4cM4Eebh4bF79+7ff/997dq1X3zxBZ/PP3PmTE5ODrEzm83GdWcQQt7e3t9///3evXvnzJnD5/OPHz8ueHYlJSUHBwf0vxWyMXybqKenZ21tLf4tKCgoBAQEIIRiY2OTkpJ2796dmppKJpODg4MFc3ni5eTk4GTc+PHjf/zxx4iIiL179+LY7t+/L9gBECGUnJyckJCgq6sbEBBw4MCBoKCgUaNGIYRevHhx4cKFvl00jMfjhYeHNzQ0WFlZeXl5OTk5BQQEnD17dvTo0QihuXPnnj17NiQkRNqAAQAAAAAk19zcLHpvVltb++TJE4QQsYoCpwJTU1O5XK7Qzunp6bgDMp4wqKysjCchEs2IBSUmJnYbBs5/VVRUZGVl4efKol1KROGaNqLzAQ0NDTdu3Ih/1tPTc3R0FH+czs7OnTt37tixIy8vT3Dc0tJy7dq1CCE+ny+Y+mxpacnNzRU6SFNTU2ZmJkLIyMgIj0h+0aQlbcAAAADAwIMZiINYe3u7mZnZr7/+ioslm5qaOjs7h4eHp6SkJCcn+/v742KCeXl5OCe1dOnSBQsW4IerI0aMmDRp0q5du969e3f27FlHR0c8fvfuXbxYY+fOnZMmTcIncnBwsLW1/eWXX4QCcHZ2zszMzMvL43A4uPEchhOIbm5ukqzG9fDwePToUW5u7tGjRxFC2tramzZtsrOzk/w64FyehYXF999/jxeJmJqa2traBgYG1tXV5efnCx6tpqbGwMBg//79uKiNjY3NlClTwsLC0tLSEhIS5s2bh8elumhYY2NjU1PTxo0bp06dKv6NSxXwh7B27drs7OwPegoAPhPEjGMAAPh4XLx40dramijVx+FwoqKiuFyuqqoq0UnZ3d09JSWltrb25s2b8+fPJ17b1NR0+fJl9L8dk93d3W/evPnkyZOMjAzi/hAhRKPR8F2fKENDQ2NjYzqdfuLECYTQqFGjcI8R8UxMTBBChYWFFRUVQpk4XPqQy+UyGIy0tDTx6UgFBYWGhobq6uoHDx7Y29sL3pjhUjlIZKrgxYsXR44cqa6ujn/t6uo6ffo0h8Mhk8mzZs0iroPkF00SPB6vzwEDAAAAAwwSiIPbsmXLBFutkcnkVatWZWRkcDgcKpWKJ/elpqYihHR1df39/QVvRzQ1NRctWhQaGkqn08vKyvDtzu3btxFCEyZMELw7RAg5OTlNnDgRP4YVHJSTk+vo6KDRaLgkDZJm/TJWX1+PH9gihJSVlY8cOSJt9UO8YMTIyEiwxIyCgsL69evr6upEa2wvWbJEsFS2vLz86tWrs7KyOjs779+/j+vLSHXRCB4eHkKrufsl4H5XVFQk+pgdANBnK1aswJOIAQBA5rS0tOh0+vbt2319fc3MzOh0OpVKxYsq/Pz8iDWwjo6OY8eOzc/Pv3jx4rt371xdXSkUSklJybVr13C1xFWrVhG3QHPnzk1MTGxubj5w4IC3tzeuY5Ofnx8bGysnJ6eurt5tWUBnZ2c6nY4ftEh4W2hra6usrIwXxGzfvh0/C+/s7Lxy5cr169cRQurq6i0tLREREb1WVHRycoqLi0tPT4+IiJg6daqWllZra2t+fv6NGzfwiQSXA5NIpDdv3mzbts3X13f48OEVFRUPHjx4+fIlQsjLy4tYxC3VRRMP7/b333/X19crKChoaGhIFTAAAAAw8CCBOIhpamqOHz9edHDixImpqalEF+N3794hhNzd3YlOxwRXV9djx45xOJzKykozMzMWi4WnHxJPpwV9+eWXQglEdXV1W1tbGo2WlZVFJBDxg2gJH8BmZmYePXqUxWLp6+szGAw2mx0dHb1+/Xq8NT8/v6OjY+TIkeK71xkaGubk5GRkZIwZM8bDw0NZWRmPd/t9XllZWfSRNYVCmTBhQnp6+tu3b/GI5BdNcNOUKVN6fcvSBgwA+Pj14ckHAAB8IHPmzKHRaE+fPo2KihIcd3V1nTdvHvEriUTatm3b3r17S0tLk5OTk5OTBTcFBAQI3mRqaGiEhIT8+OOPLBbr7t27d+/exeNycnJBQUFUKrXbBKKLi8u1a9fwAV1dXSUJXkdHZ/ny5adPny4vLw8KCjIzM1NXVy8pKWlpaUEIzZ4929/fPzg4mMlkHjx4MDQ0VHAFjJDly5c/e/aMTqcnJiYKrbM2MDDYtm2b4Iijo6OSklJ6ejqeL0mwsbFZvny54JWR/KKJZ2Ji8uzZs+fPn69cuRI32ZMqYAAAAGDgQQJxENPX1+/2ISd+HltdXY1/xZWeu53XRiaT9fT0Kioq8D5EaRXRCtk9DTo7O9NotCdPnhCVp4k62b0+gE1PTz98+DCPx5syZcq6deuoVOq///3vhISEESNGeHp68vn8X375hc1mnzhxQnwC0c/PLykpqaWlJTIy8uzZsxMmTBg9erSdnV23b1lfX7/btn34ohFVsSW/aIKINoXiSRXwh/Dw4cOBOREAAAAABpi8vPwPP/xw9+7dxMTEyspKeXl5MzMzLy8v0XszTU3Nw4cPU6nUR48e0el0NptNoVBsbW19fX2JUomEkSNHnjhx4vr169nZ2fX19crKyiNHjpw/f/6oUaOoVGq3kVhYWOjq6tbV1dnZ2RHrqXv19ddfKykpnTlzhsViEb3sVFVVAwICpk+fTiKRgoOD9+3bV1ZWdurUKeKpsyi8roVKpaampjIYDCaTqa2tra+v7+bmNn36dAUFBcGdyWTy9u3bExISqFQqnU4nkUgmJiZTp0718vISum+U6qKJsXDhQtxAj8fj4cXdUgUMAAAADDxIIA5iPU14wbdouP40l8tlMpnEYLc7V1RUMBgMhBDeEyGkra0tume3g87OzlFRUU1NTYWFhdbW1pKvX25ra4uIiODxeJ6enuvWrSORSD4+PoWFhWlpaVFRUWZmZhoaGrjYTa9pNW1t7ePHj587dy49PZ3FYqWkpKSkpCCELC0t586d6+rqKni7LLh4WRAex8tPpLpogsQnOvsWMAAAAACAJFavXr169Wr8s4+Pj4+PT68vkZeX9/b29vb2luT4FApF8BSEgwcPdrt/Z2dne3s7knj9MkYikWbOnDlp0qTS0tLy8nIul2tgYGBvb0+s2Bg/fjwuudMrRUXF2bNnz549W8Lzenp6enp69rqnhBdt6NChYuJUV1fftWvX+wQMAAAADDDowjyIdbtaBP03C6avr48QIpPJmpqaYnbGmTKciyQm0HXbFqDbI+jo6FhZWaH/9mLG0w97rUqDEPrrr79YLBaFQgkICMD5MhKJFBQUZGxs3NXVdeDAgdjYWISQjY1NtxMGhVAolE2bNl26dGnXrl3e3t64Yk5JScmhQ4dwTWuCaF8/DK/dxhlDqS6aIMGahv0VMAAAAADAYJSbm9vW1iYvL487MktFTU3Nzs7Ox8fHz8/PxcWFyB4CAAAAQFYggTiIVVVV8fl80fGKigoksPwWZxJF19sihHg8Hm7ogdu6ESWiifqJQqfrNgxnZ2eEEC6PSKxfliR4hJC1tbWSkhIxqKysvHv3bhUVlbq6unv37iGE/Pz8ej2U4MtdXFwCAwMjIyPDwsLGjRuHEIqJiWlrayP2qamp6fai4bdMtLeT/KK9D0kCBgAAAAAYdPh8/p07dxBCjo6O0P0DAAAA+ARAAnEQa25uxo92qS4AACAASURBVPP+BNXW1j558gQhRNRhwbULU1NTuVyu0M7p6em4AzKeMKisrIwnId6/f1/0dELlnAn4qXJFRUVWVhZevyzapUQUbk4iOh/Q0NBw48aN+Gc9PT1HR0fxx+ns7Ny5c+eOHTvy8vIExy0tLdeuXYsQ4vP5gqnPlpYW0QbETU1NOAFqZGSERyS/aNKSNmAAAAAAgEGkoKAAdyN5/vw5QkjC9dEAAAAA+MhBAnFwu3jxouAyWw6HExUVxeVyVVVViU7KeD5gbW3tzZs3BV/b1NSEV8sKdkzGOz958iQjI0NwZxqNhmcXijI0NMQrcHHfulGjRuFS0OKZmJgghAoLC/F8SUG49CFCiMFgpKWliT+OgoJCQ0PDy5cvHzx4IDS18PXr1/gHoamCFy9exL38sK6urtOnT3M4HDKZPGvWLDwo1UWTBI/H63PAAAAAAACDRXp6+sGDB/HTaCcnJzs7O1lHBAAAAIB+AE1UBjEtLS06nb59+3ZfX18zMzM6nU6lUnG7Oj8/P2K1iKOj49ixY/Pz8y9evPju3TtXV1cKhVJSUnLt2jVcLXHVqlVE1465c+cmJiY2NzcfOHDA29vb3t5eUVExPz8/NjZWTk5OXV2927KAzs7OdDodV06UsE62ra2tsrIym80+cODA9u3bcQqys7PzypUr169fRwipq6u3tLRERET0WlHRyckpLi4uPT09IiJi6tSpWlpara2t+fn5N27cwCcSXDhDIpHevHmzbds2X1/f4cOHV1RUPHjw4OXLlwghLy8vYhG3VBdNPLzb33//XV9fr6CgoKGhIVXAAAAAAACDiLGxMYVC4XK5bm5uK1asgNZwAAAAwKcBEoiD2Jw5c2g02tOnT6OiogTHXV1d582bR/xKIpG2bdu2d+/e0tLS5OTk5ORkwU0BAQHjx48nRjQ0NEJCQn788UcWi3X37t27d+/icTk5uaCgICqV2m0C0cXF5dq1a/iArq6ukgSvo6OzfPny06dPl5eXBwUFmZmZqaurl5SU4LmBs2fP9vf3Dw4OZjKZBw8eDA0NVVRU7OlQy5cvf/bsGZ1OT0xMFFpnbWBgsG3bNsERR0dHJSUlvLJGcNzGxmb58uWCV0byiyaeiYnJs2fPnj9/vnLlSisrq9DQUKkCBgAAAAAYRLy8vIh1MB+/FStWrFixQtZR9D95eaQgD6nbvpMjIUU5sgJ8V35vfD7qpvw8AGBwgn8UBzF5efkffvjh7t27iYmJlZWV8vLyZmZmXl5e7u7uQg97NTU1Dx8+TKVSHz16RKfT2Ww2hUKxtbX19fUlSiUSRo4ceeLEievXr2dnZ9fX1ysrK48cOXL+/PmjRo2iUqndRmJhYaGrq1tXV2dnZ4cbGUvi66+/VlJSOnPmDIvFwhMnEUKqqqoBAQHTp08nkUjBwcH79u0rKys7derU+vXrezqOsrLykSNHqFRqamoqg8FgMpna2tr6+vpubm7Tp09XUFAQ3JlMJm/fvj0hIYFKpdLpdBKJZGJiMnXqVC8vL6F2z1JdNDEWLlzIYDBoNBqPx8OLu6UKGAAAAAAAAKk0NnPYXLasoxjE1FQUEJ/P6eLJOpDBTQ4hZntXcQVT1oEMbl3cj/rPYVlZWV5eXkVFRWtrq7a29tixYx0dHcXM/gGDGqnbjrQASIXD4axYsaKtrS0oKGjGjBlSvba1tbW0tLS8vJzL5RoYGNjb2ysrK3+IIM+fP3/jxo2JEyfu2bPnQxz/Q+NwOAkJCQ4ODrjBCwDgE9DV1YWfGYwZMwZ3GwBitLW1qampIYQcHR1zcnJkFUZhYaG1tTVCyNfX988//5RVGFLhcrn37t1DCI0dOxbXIAYAfKoaGhoyMjJsxk5QGaIm61gGMUgg9gtIIPYLTs1zPq/LysrKyspK1rH8j7i4uAMHDgj1TkAI6enpbdq0aevWrYNrZgyegxURESFm6hKAGYigH+Tm5ra1tcnLy+OOzFJRU1Ozs7ODAtsAAAAAAAAAAMBHrqOjIyAg4NKlS8SIgoJCZ2cn/pnBYOzevfvOnTv3799XV1eXUYwfkbS0NDc3N4SQnZ3ds2fPZB3Oe4EuzOB98fn8O3fuIIQcHR2h+wcAAAAAAAAAAPBJ6urqWrx4Mc4e6uvrHzly5O3bt2w2m8ViFRQU/Pbbb5qamgihjIyMpUuXfm4LXvfu3UsikXR1dWUdyIcCMxBB3xUUFDQ2NtJoNLzyztvbW9YRAQAAAAAAAAAA4IMIDQ2NjY1FCE2cODEuLg5X+UcIqaiojBo1atSoUd98842jo2NVVVVcXNytW7fmzJkj03hBf4IEIui79PT0uLg4/LOTkxMsQwYAAAAAAAAAAD5Jb9++3bdvH0LI0NDwzp073U61Gzp06NWrVz08PBBC586dgwTihAkTqqqqEELy8oM+/zbo3wCQIWNjYwqFwuVy3dzcVqxYIdT6GQAAAAAAiPf69euNGzcihK5fv47bViYnJ1+4cAEhtGrVqsmTJ0t7wPd8ef9iMBhXrlwpLCxks9kWFhYLFiywtLSUbUgAAAD6LCoqqr29HSF04MABMQt13d3dx44dm5+fT6VS29raPvNCZ4qKigYGBrKOon9ADUTQd15eXufPn7906VJgYOAHap3cj1asWHH79u1B2oIZAAAAAJ8JNptdX19fX1/f0dEx8C/vRwUFBd99911iYiKdTmcwGJmZmVu3bk1LS5NtVO8pJCTEx8fnjz/+kHUgAAAw0Hg83vnz5xFCX3zxxaJFi8TvHBAQMHr0aAsLi9zcXKFNr169+u6776ytrYcMGaKmpmZtbb1u3boXL16IHkRNTY1EIt2/fx8hdO3atbFjx8rLyx8/flySrRiTyfz1118nTpyora2tqqpqY2Pzj3/8IyMjQ6rijGlpaQsXLrS1tdXS0lJTUxs1atTs2bNv3brF5XKJfdasWUMikX766SeEUH19PYlEIpFIoaGhgqH+8ssvogeX9mr8+eefCKHU1FQ/P79hw4apqKjY2Nj885//LCsrk/wd9RnMQAQAAAAAAAD0p46OjiNHjrDZ7BkzZixbtoxEIl2+fJlKpZ48edLBwUFVVVXWAQIAAJBOSUlJRUUFQujrr7/udTXuhg0bNmzYIDoeFha2fft2wdRbYWFhYWFhVFTUb7/9tn379m7XNR47diw4OLinc/W0NS0tbe7cuQwGgxh5+fLly5cvL168+M9//vPEiRN44r8YnZ2dS5YsuX79uuDgq1evXr16defOnVmzZt2+fft9Fib3+Wps3LiRyIHiN3X58uXk5GQXF5c+ByMJSCACAAAAAADwsZg1a9asWbNk9fL+UlRUVFNTo6mpuW7dOvzlas2aNTQarbq6uqioaNy4cbIOEAAAgHSys7PxD30uRnHu3LktW7YghOTl5ZcuXYqzXZmZmdHR0Z2dnTt37tTW1l69erXQqzIyMn799VeEkLGxsaWlpZWVlSRbX7x4MXPmzPb2dgUFheXLlzs5OampqT1//vzs2bMMBuPMmTNsNjs6Olp8wMeOHcPZQ0NDw4CAAHzwoqKis2fPvn379t69e8eOHdu8eTNCKDIy8vjx4/v27fv555+1tbVramoQQnJy4pb89u1q4Edx5ubmGzZsGD9+fH19/enTp+/evdvR0bF06dLS0tIPWlkOEogAAAAAAACA/oRnqejr6xNTM8hksrGxcXV1dWVlJSQQAQBg0CEWyZqamvbh5c3NzXieIIVCiY2NxV1WEEKBgYGrVq2aM2dOfX39li1bFixYoKmpKfjC3377TV9f/+TJkz4+PqKH7XYrn89ftWpVe3v7sGHDUlJSRowYQWzatWvXokWLqFTq5cuXv/nmmy+//FJMzDdv3kQImZiY5ObmCtZ83LBhg52dXUVFRXJyMk4gkslk9N+MIYlE6nVaYp+vxsmTJ11cXO7cuaOtrY1HfHx85syZc/v27Tdv3rx588bc3Fz8qd8HJBABAAAAGSsqKrKwsJB1FB87qarVAPChVVVVBQYG6ujonD17tqqq6vTp0/n5+Z6enoKTBfLy8u7fv//q1avW1lZdXd0RI0bMnj3b2tpakiMjhC5duqShoUGMs1ismzdvZmRk1NTUDBkyxMHBYdGiRXp6en5+fgihmJgYXJC6p5dzuVwqlfro0SM6nc5msykUiq2trY+Pj9A3DfxydXX16OjoxsbGmJiY7OzsxsZGPT09GxubJUuWiKmaLwh/vSwrK+vs7FRQUEAIdXZ2vn79GiFEoVAkOQJCqK6u7p///CcOpqSk5OrVqy9fvuzo6Bg6dOjXX389c+ZMEon08uXLa9euFRYWcjgcQ0NDT09PT09P/BUuMzPzt99+I5FIFy9eFLwU2IYNG8rLy729vfHlQggVFBTcunXr7du3DAZDU1Nz2LBh3t7eEydOxLM5jh49mpSUhPeMjo6Ojo62srIi6lshyT7unv7Y2NraShUqAAAMvMbGRvxD3xKIly9fbm5uRgh9//33RL4Mmzx5ckhIyMaNG1taWi5fvrx27VrBrVwu9/Lly1OmTOn2sN1uffz4cVZWFkLo/PnzgtlDhJCWlhb+B7yhoeHSpUtiEogcDgcXcFyxYoXQ/306OjrTp0+/cOFCcXFx7++8O32+GgihyMhIInuIECKRSIGBgbdv30YIFRcXQwLxEwFd9gAAAAjx8PAoLi6urKzE36tBr7766isvLy9ZRwHA/1dbW7tr166GhgbBQR6Pd+LECSqVSoxUV1dXV1enpqbOnz8f1wSU6iw1NTV79uypra3FvzY1NSUlJWVlZf3444+SvLy5uXnv3r0lJSXECIPBSEpKSk5OXrly5Zw5c0RfUlVVtWfPnrq6OvxrZWVlZWVlRkZGeHi4vr5+r2e0tLTU0NBobm6+dOnSypUrORxOeHh4fX29urp6H6Yf5uTk/Pbbb11dXfjX8vLyyMjIlpYWbW3t8PBw4unC69evT5w4UV9fv2zZMoSQvb29oqIih8PJycmZNm2a4AHpdHp5eTlCyN3dHY+cO3cOzzQhrg+DwaDRaF5eXuvWrUMIqamp6ejoMJnMrq4uFRUVVVVVYlZIHz5uoT82UoUKAAAywWaz8Q+SPwcS9PDhQ4SQurp6t89CAgICQkJCWlpakpOThVJmjo6OQim2Xrfeu3cPIaSrqyv0Lyqmo6Pj4eERGxv7119/iQlYUVGReMuiWltbxby2V32+Gq6urvb29kL7Dx8+HP/woR+3QwJRlnCbPITQ+3TZ6/PL+1FBQcHevXuJv10MBuPJkydbt26VeVrzfYSEhNBotKVLly5cuFDWsQAAPlmPHj1CCJHJZNEpJ6BbS5cuXbJkiayjAOA/eDxeeHh4Q0ODlZWVubm5k5MTHr9y5QpOJ7m4uEyZMuWLL76g0+m3b98uKSm5du2ahoaGr6+v5Gfh8/mHDh2qra0lk8lz584dN24cj8d78uRJXFzcgQMHJHn54cOHcfbQw8PD1dVVR0enpKTkxo0btbW1Z86cMTIyGj9+vOBLuFzu4cOH29vb8SotHo+XnJx89epVFot17ty5HTt29HpSBQWFgICAsLCw2NhYU1PT+Pj4oqIiMpkcHBysoqIi+XtHCLHZ7MOHD5uZmX3zzTcmJiaFhYWRkZGNjY3R0dF8Pn/YsGEBAQGWlpZlZWWRkZHV1dU3b96cP3++kpKSkpKSg4NDZmZmVlaW0HfI9PR0hJCenh6eIZiTk4Ozh+PHj/f29tbV1a2vr799+zaeVOjq6mpnZxcQEIC/0dFotLlz5wreH0r7cYv+sZE8VAAAkBUtLS38A546J62///4bIWRlZTVkyBDRrUOGDBk5cmROTk5+fr7QpjFjxoh56tbtVjxzsK6uDq8s7gnxWE5CPB6vsrLy8ePH8fHxgs+c+qDPV2PkyJGi+4svttiPIIEI3hd02QMAgPc0atSo58+fyzoKAIDUGhsbm5qaNm7cOHXqVOILTF1d3bVr1xBCgo8hLSws3NzcDh069Pjx48uXL0+bNk1dXV3Cs+Tk5OBFUiEhIQ4ODnjQzs5u+PDhERERvb48Ly+PRqPheBYsWIDjHDFixKRJk3bt2vXu3buzZ886OjoKfgFjsVjl5eXh4eFGRkZ4ZPHixUwmMz4+/tWrVxKG7eHh8ejRo9zc3KNHjyKEtLW1N23aZGdnJ+HLCZ2dnV988cX+/fvxCp4JEyZwOJxDhw5xuVwtLa3Q0FD87cvOzm7Dhg179uzp6uqqqqrC0zGcnZ0zMzPz8vI4HI5gt02clXNzc8PvOicnByFkYWHx/fff469hpqamtra2gYGBdXV1+fn5YsLuw8fd7R8bCUMFAABZ0dPTwz+8e/dOkv2JaeO4ICBeAU3MlRM1fPjwnJwcYqE0YejQoWLO0u1WYvq8eCwWq6urS3y9wvr6+piYmIcPHxYWFpaUlIiZkyiVPl8NY2PjfgmgbyCBKEvQZQ8AAAAAYFDz8PAQmjKWkpLC4/GMjIzmz58vOE4mk9etW5ednd3e3v78+fNJkyZJeIrY2FiE0Pjx44nsITZjxgxcs0/8y1NTUxFCurq6/v7+gkkoTU3NRYsWhYaG0un0srIyMzMzwVd5enoS2UNs/Pjx8fHxol9melJfX8/hcPDPysrKR44c6duqN4TQ/PnzBXNqo0ePxj/4+fkJzt2wsbGRk5Pj8XhMJhOPODk5ycnJdXR00Gg0Yn6o6KJgPAnFyMhIcBKHgoLC+vXr6+rqDAwMxMTWt49b9I+NhKEKqq2txauRBEG5WADAB0L8H5SdnT1v3jzxO1dXV+PUnqura1pamiTHx8kEIu1I6HaOnvit+CDjxo2Li4sTf1Lxc/euXbv27bffNjU1ESMmJiYjR450cXHJycmJj48Xf/D30dPV6LU9ywc1QBMdwSespy57CKHKykpZRgYAAAAA8IGJlnXHGR97e3vRryWampo4T1dUVCT5Kd68eYMQmjp1qtA4iUTqqai8IDxVxN3dXfRbh6urK07Mid6zETksguRTJhFCmZmZ69evf/78ub6+vpycHJvNjo6OJrbm5+dnZ2dLvghOqM0U8XVRqOI2mUzGnWQEY7a1tUUI4Wr6GJ7TZ2hoSORMDQ0NEUIZGRlUKlVwdomDg8PMmTPHjh0rJra+fdyiH5yEoQpqamp6K0LyDC8AAEjF0dERN8W6detWr88qiK5T+F829N/KifjfzG7h/+wE24P0GT4InU436o2YBOKzZ88WL17c1NSkpaW1e/fuv/76q7W1tby8PCEh4aeffnrPmYADeTX6EcxA7B102RMPuuxBlz0AAADgsyV6v1RVVYUQiouLEzPxQfLi6y0tLW1tbQihbluXSNLPpKamBiHU7TQ6Mpmsp6dXUVGB9xEkftqdeOnp6YcPH+bxeFOmTFm3bh2VSv33v/+dkJAwYsQIT09PPp//yy+/sNnsEydOSFj+tafpJ+KnpWDOzs40Gu3Jkyd8Ph/f6eGsnLu7OzEf08/PLykpqaWlJTIy8uzZsxMmTBg9erSdnZ0kF6FvH3e3t9mShCrIysrKyspKaLChoSEjI6PXsAH45DXUM9isNqFBio6uiqrae279bKmqqvr5+cXExBQWFiYmJorpX4wQIvpKEU+/Ro8e/erVq6KiIhaLJVrorL29HZfIIOaYv4/Ro0c/fPiwvr6+oqICPyIScuvWrcbGRjMzMzHtWaKiorhcLplMTk1NHTNmjNDW92yiMpBXox9BAlEK0GWvW9BlD7rsAQAAAJ8t0RQYg8FACMnJyYmp3U6s7e0VcYdGVK8X1O2gIC6Xi9fz9rSnlpZWRUUFjlmQJLm5brW1tUVERPB4PE9Pz3Xr1pFIJB8fn8LCwrS0tKioKDMzMw0NDTabTSaT3ydHKTlnZ+eoqKimpqbCwkJra+tub7e0tbWPHz9+7ty59PR0FouVkpKSkpKCELK0tJw7d66rq6uY+/m+fdzdZk4lCRUAIKFTR/cl3YsVGtyx79j0r+a+59bP2XfffRcTE4N/yMvLU1PrPqOan5+PZ52rqqp+9dVXeHDatGk3btxobm7+17/+FRQUJPSS06dP4/+tuu2bLK0ZM2YcO3YMIbR///7jx48LbS0uLvbz8+Pz+SdOnBCTQMRJFSsrK9HsIY/HE9/BuVcDeTX6ESQQJQVd9noCXfagyx4AAADw2RJd/UShUBobG5ctW9ZriShJEIk/JpP5xRdfCG3tdRUwmUzW1NRkMpmCJZwE4a8ofa5OKOqvv/5isVgUCiUgIADn3UgkUlBQUHl5OZ1OP3DgAL6LtrGxEd8cs7/o6OhYWVkVFRVlZWVZW1vj2y1zc3OhOSkUCmXTpk1r1659+vRpfn5+fn4+nU4vKSk5dOjQwoULly5d2tPx+/Zxd7toTsJQAQBAVtzd3ZctW3bp0qXi4uKvv/46NjZW9L+P+vr6gIAALpeLEAoODiaSjEuWLNmxY0dra+u+ffucnJxcXFyIl2RkZOzbtw8hNGTIkOXLl79/nLNmzRo+fHhZWVlUVNS0adPmzv3/mV8Wi/XNN9/w+XxFRUWh2rVCTExMEEIVFRVCkwT5fP7mzZvpdDrqrkYhQqi9vR1PXRRz8IG8Gv0IEoiSgi57YkCXPeiyBwAAAABs6NChr1+/lrBJZa+0tLSUlZXZbHZNTc2IESOEtoouPRalr6/PZDK73ZPH4+FVL+J7XEoFL+m1trZWUlIiBpWVlXfv3r158+a6urp79+4hhHBlnoHh7OxcVFSUmZm5YsUKYlFwt3sqKyu7uLjgL3IlJSUXLlx49uxZTEzMnDlzepqS2b8ft+ShAgDE2/lzxM6fe/wG/T5bP3Ph4eE0Gu358+ePHj2ytrbevn27n5+fqakpj8d7/fp1Wlrarl278NTskSNH7ty5k3ihlpZWWFjYt99+W19f7+HhsXLlyokTJ/L5/KysrHPnznV2diKE3qfdliB5efl//etfX375ZVdXl7+//7x586ZMmWJgYFBUVBQVFYUnd0dGRuro6Ig5yPTp08+cOdPc3Ozj4/P9999bWFg0NTXl5+eHh4dnZ2fjfUpLS69fvz516lR8KFzSjcViRUREuLu76+rq4iykqIG8Gv0IEohSgC57PYEuex+6y17/8vX1xX9UAPjknTx5csGCBbKOAgDwecHzxZ49e8Zms4V6erS1teEnoBs3bhRqANITEolkbGxcXFyckpIyefJkwU18Pl+S1pbDhg0rKipKTU1dtmyZ0ISI9PR0fAvXj3PccKsW0amRhoaGGzdu3L9/P0JIT0/P0dGxv87YKxcXlwsXLlRUVGRlZeHbLTc3N2JrZ2dnSEgIn89fuHCh4B24paXl2rVrAwMD+Xx+VVVVT59X/37c4kMFAACZ09bWfvjwoa+v7+PHj2tra7du3bp161Y5OTkSiYRnHWJWVlZ37twRKtcQEBDQ0NCwe/fuzs7OU6dOnTp1itgkJyd34MCBgICA/opz+vTpf/zxx4oVK9rb269fv379+nVik4KCQkhISK/nWrx48Z9//nnt2rWHDx8+fPhQcJOXl9c//vGPJUuWIITmz5/v6+v7559/IoH+Y5s2bUIIHT58eOvWrT0dfyCvRn+BBKIU+tBlr7i4uKioSPIEovguexcuXBD/cvFd9o4dO8bhcCorK4USiO/fZe/o0aMsFktfX5/BYOAue+vXr8db8/PzOzo6Ro4cKWGRbKm67LFYLMGYbW1taTRaVlYW8Y667bKXk5OTkZExZswYDw8P4j5PKGPbrb593D112es11A+ntbUVOvSBz8TFixcZDMZ3330n60AAAJ8Rd3f3mJiYhoaGK1eufPPNN8QzXT6fHx0d/fr1awqFItTaTjwfH5/ff//9yZMnNBpNcKnEX3/9JVj2Wkw8KSkptbW1uPYLMd7U1HT58mXU37cfeLZFYWGhaOl6XPqQy+UyGIy0tLQBS40ZGhoaGxvT6fQTJ04ghEaNGqWnp0dsVVBQaGhoqK6ufvDggb29veAzeNwVEInM0OTxeMTP/ftxiw8VAAA+Bnp6emlpaadPnz5y5EhhYSH6338VNTQ01q5du3v3btEMAIlE2rFjh7e39/Hjxx8+fFhRUYEQMjQ0nD59elBQkI2NTf/GOX/+/EmTJh07duzevXtlZWUIoZEjR9rb2+/YsUMo7dAtEol09epVPz+/qKio4uJiPN9o8uTJ33zzzYwZM0gkUm1tLe7fQKwPmDlzZmhoaERERFVVla6urvi2tAN8NfoFJBClAF32REGXPSEfqMte/1JTU/vY5kID0O/4fH5TU9OdO3cggQgAGGAmJiYzZ85MSEiIjY2l0+lffvnl0KFDGxsbExIS8H/3y5cv77YEXk/c3NyuX79eXl6+d+/e+fPnjx07ls/n5+bmxsbGGhoa4q8cYg7o6Og4duzY/Pz8ixcvvnv3ztXVlUKh4PLNuEPLqlWr+vH2w9bWFq+5PnDgwPbt242NjRFCnZ2dV65cwRNA1NXVW1paIiIiBrK6n7OzM51Ox03tRFd7ODk5xcXFpaenR0RETJ06VUtLq7W1NT8//8aNG/gdETei+EL9/fff9fX1CgoKGhoa/f5xiw8VAAA+BnJycoGBgd9+++2LFy/y8/Orqqo4HI62tvaYMWOcnJwE1xSKGjNmzMmTJyU5i/hciiSZFkNDw4MHDx48eLDXPYl+rYLk5OQWL168ePHibl8SHBwcHBwsNLhly5YtW7ZIHmq/XA1ra+tu4+93kECUAnTZEwJd9kQNii57t27dGoCzACBbTCaz1382AQDgA/n222+bmpqePHmSk5OD6y9jJBJpwYIFM2bMkOpocnJy27Zt27NnD5PJvHr16tWrV/H4sGHDQkJC1qxZo6ysLObbGolE2rZt2969e0tLS5OTk5OTkwU3BQQECHXYe086OjrLly8/ffp0eXl5UFCQmZmZurp6SUlJS0sLQmj27Nn+/v7BwcFMJvPgwYOhoaHiNbqcsgAAIABJREFUv2f2FxcXF1zJmkQiubq6Cm1dvnz5s2fP6HR6YmJiYmKi4CYDA4Nt27YRv5qYmDx79uz58+crV660srIKDQ1F/f1xiw8VAAA+HiQSacyYMaJNisGnChKIUoAue0Kgy54koMseAAAA8LlRVFTcs2dPWlraw4cPi4uL2Wy2gYHB8OHD/f39pVq8TDAxMTl+/HhMTExmZmZjYyOFQnF1dV20aBG+P+z1eYmmpubhw4epVOqjR4/odDqbzaZQKLa2tr6+vn2LR7yvv/5aSUnpzJkzLBaLWGStqqoaEBAwffp0EokUHBy8b9++srKyU6dOEXVvPigLCwtdXd26ujo7OzvRy4VLeFOp1NTUVAaDwWQytbW19fX13dzcpk+fjoviYwsXLmQwGDQajcfjEYuL+/fjFh8qAAAAICuQQHwv0GUPQZe9Dx8qAAAAAD42Q4cOvX37tpgdSCSSm5tbr2X+zM3NhY7T05E1NTVXr169evVqwUFcPlswzdTTy+Xl5b29vb29vcXHI+Z9jRw5UvxbJpBIpJkzZ06aNKm0tLS8vJzL5RoYGNjb2xO1p8ePHy/JoXR1dbvdTVFRsaeXE9MzhXR2dra3t6Oeb7cUFRVnz549e/Zs8SGpq6vv2rVLdFzCj7vXPzaShAoAAADIhBT1OIAowbZrQpva2to2bty4fv16SYpbY7jLHkIIL6oVJHmXPYRQamqqYP8jbOC77OGfB77LHkJITJe9nTt37tixIy8vT/BVuMseQgh32evp4P37cYsPFQAAAAAAi4uLO3r0qFALSOzp06cIoY+zz4aampqdnZ2Pj4+fn5+Li4tQh+IBlpub29bWJi8vj2/APmaDKFQAAACfFUggvhf8YBC3XRMsWkm0XWtpaZG2yx5CCHfZExyXvMseQgh32RMcH4Aue0KbcOlDhBDustdfZ+wVbl2HEBLTZe/ly5cPHjwQKjIqYZc91H8ft/hQAQAAAAAwNpudlJR09epVoVLLLS0t8fHxCCEHBwcZhTY48Pn8O3fuIIQcHR37XPt7YAyiUAEAAHxuYAnze4Eue9BlD7rsAQAAAOCDcnV1jYmJqamp2b9//8qVK42Njdvb24uKiv7v//6vtbVVW1sbWm30pKCgoLGxkUajPX/+HCHU6wpuGRpEoQIAAPg8QQLxfUGXPeiyh6DLHgAAAAA+mGHDhgUFBYWFheXm5ubm5pLJZKJYjaKi4pYtW2S7Ovhjlp6eHhcXh392cnKys7OTbTxiDKJQAQAAfJ4ggfi+oMsedNmDLnsAAAAA+KDc3NxGjBgRExNTXFxcXV0tLy+vo6NjZ2fn5+dnYGAg6+g+XsbGxhQKhcvlurm5rVixoh8X4vS7QRQqAACAzxNJqBIc+Gi9ePFi165d1tbWhw4dknUswlpbW3vqsjfwOBzOihUr2tragoKCpJ0SOMCkCpXD4SQkJDg4OOBWOQAA8ZhMJs7LT5w4MTMzU9bhdK+rqws/txgzZgxeswY+foWFhdbW1gghX1/fP//8U9bhSITL5d67dw8hNHbsWFy/GADwqWpoaMjIyLBzdB4yRF3WsQxiKkpkPh91cXm97wp6RkKIxeG+q2uTdSCDW01JDo/bZWVlZWVlJetYwOcOZiB+XOLi4kpLS21tbadPny606ePvsveRLLUYRK3rBlGoAAAAAABgsNBURBqwrv19kEmoqxOhTlnHMdjJqWup6msqyTqMwS3hjRyHK+sgAEAIQQLxY4O77L148cLNzU2wYiB02ZPQIGpdN4hCBQAAAAAAgwmHgzrYsg5iMFNSQqxWuIbvS04OqaggHkzkBOATAQnEjwt02euzQdS6bhCFCgAAAAAAAAAAAAAJxI8LdNnrs0HUum4QhQoAAAAAAAAAAAAgJ+sAgDA3N7cTJ07MmDHD1NRUXl5eSUlp2LBhs2bNOn78uK2trayj+3jh1nUaGhre3t7btm37mFvXDaJQAfgEZGVlkSTj5+cn62ABAAAAAAAAH0ptbe13331nYmKipKRkbm6+detWJpMp4WszMjJ8fX0tLS3V1NQcHR2Dg4MbGhr69xQfOZiB+DEyMDAICgqSdRSDjJeXl5eXl6yjkMggChWAQc3Nza2wsLC2tlbC/Z89exYYGHjgwAEKhfJBAwMAAAAAAAAMsIqKCmdn53fv3iGE1NTU3rx58/vvv9+9ezcjI6PX+/9jx45t2rSJx+ORyWRtbe28vLy8vLwrV66kpqaOHDmyX07x8YMZiAAAAD5NqamptbW1+NFfr1RUVMrKyk6dOtXW1ibrwAEAAAAAAAD9bMOGDe/evRs3btyLFy+am5tzc3OHDx/+6tWrXbt2iX9hRUXFjh07+Hx+aGhoW1tbbW3tmzdvXF1dGQzGmjVr+Hz++59iUIAZiAAAAD5l48aNy8zM7HW3L7/8MjExcQDiAQAMIq9fv964cSNC6Pr164qKirIO5xPHYDCuXLlSWFjIZrMtLCwWLFhgaWkp25CSk5MvXLiAEFq1atXkyZPfczcAABi83u0JbU5Mk3UUCCE0dHsgxX9WH17IYDDi4uJUVFRu3rxpZmaGEHJwcLh586aDg8PVq1ePHj0qpuFEZGQkm82eN2/eli1b8Mjw4cOjo6MtLS1TUlLodLqJicl7nmJQgAQiAAAAAAAAQJYKCgr27t3LZrPxrwwG48mTJ1u3bpVtPo7NZtfX1yOEOjo6iMGQkBAajbZ06dKFCxeK2Q0AAD4lHcVv2p7QZB0FQgh11tT17YVXr17t6uqaOXMmTu1h9vb29vb2T58+vXv3rr+/f0+vffHiBUJIaAdTU9ORI0e+ePHi5cuXOIH4PqcYFGAJMwAAAAAAAEBmOjo6jhw5wmazZ8yYce7cufPnz3t6evJ4vJMnT7JYLFlHBwAA4FOQnJyMEPL09BQaxyMPHz4U89ra2lpNTU3BtCCmqqqKEGptbX3/UwwKMAMRAAAAAAAAIDNFRUU1NTWamprr1q2Tl5dHCK1Zs4ZGo1VXVxcVFY0bN05Wgc2aNWvWrN4Xykm4GwAAABmqqalBCI0YMUJoHJfLwFt78vjxY9HBhoaGV69eIYRsbW3f/xSDAiQQ/wNq3AwkqHEDAAAAAACwiooKhJC+vj7OHiKEyGSysbFxdXV1ZWWlDBOIAAAAPhm1tbUIIS0tLaFx3BwZb5VER0dHfX09jUb75ZdfWlpaFi5caGVl1b+n+GhBAhEMNKhxAwAAAIBBrbW1NT4+Pisrq66urrW1VVtb28DA4Msvv3R1dSWTycRuVVVVgYGB6urq0dHRjY2NMTEx2dnZjY2Nenp6NjY2S5Ys0dXVFToyi8W6efNmRkZGTU3NkCFDHBwcFi1apKen5+fnhxCKiYnB9ddpNFpISIiysnJMTIzQEVJSUsLCwgwMDE6dOtWHmKUKg5CXl3f//v1Xr161trbq6uqOGDFi9uzZ1tbWEl5PU1NThFBZWVlnZ6eCggJCqLOz8/Xr1+i/X7p69dNPP+Xm5trZ2f3888/EIJfLXbRoUUdHh6Ki4h9//CH4Nvfs2fP8+fPRo0fv378ff0w6Ojpnz56tqqo6ffp0fn6+p6fn6tWr8SaE0KVLlzQ0NI4ePZqUlISPEB0dHR0dbWVlFRoaKrQb+pAfPQAAyITaZCdVR9tuN7WmZjfdTerf0+lvWKFgaNDtJgUDvb4dE+fvRP9bkTa7N3HiRBrtP+UgAwMDjxw50u+n+GhBAhEMKMEaN8uWLSORSJcvX6ZSqSdPnnRwcMAVBAAAAAAAPlp1dXWbNm1iMpnESE1NTU1NDY1Gy8jI2LFjB4lEEnpJVVXVnj176ur+U/e9srKysrIyIyMjPDxcX19f8Dh79uwhvmA0NTUlJSVlZWX9+OOPAxmzVGHweLwTJ05QqVRipLq6urq6OjU1df78+fhmr9fwLC0tNTQ0mpubL126tHLlSg6HEx4eXl9fr66uLuH0w3HjxuXm5r569YrL5RKJwtevX+MnvhwOp7S0lJgh0tXVVVhYiBBycHAQPEhtbe2uXbsaGhp6OouampqOjg6Tyezq6lJRUVFVVdXU1BQfmMw/egAA6Bf8jg5uE7PbTTw2u9+ba3BbWuV6OB2fx+3bMfl8vpitHA5HwuPY2try+fw3b960tLT88ccf9vb2+BlSP57iowUJRDCgoMYNAAAAAAa1o0ePMplMZWXl+fPn29raqqio1NTUxMfH5+XlZWRkZGdnT5gwQXB/Lpd7+PDh9vb2DRs22NnZ8Xi85OTkq1evslisc+fO7dixA+/G5/MPHTpUW1tLJpPnzp07btw4Ho/35MmTuLi4AwcODFjM0oZx5coVnD10cXGZMmXKF198QafTb9++XVJScu3aNQ0NDV9f317DU1BQCAgICAsLi42NNTU1jY+PLyoqIpPJwcHBKioqkrxBe3t7hFBHR0dxcTEx8xGXpsIKCgqIBGJpaSn+FodfhfF4vPDw8IaGBisrK3NzcycnJ9GzBAQEBAQE4BUqc+fOJVao9ORj+OgBAKBftD39u/FGfE9bSfL9nEGsvxzb0yaT8J/6dkx9ff2Wlpampiah8cbGRoSQgUH3Ex5FXbx4ESHE4/EuX768atWqNWvWGBgY4P/s+usUHy1IIIIBBTVuAAAAADB4sdns58+fI4TWr1/v7u6OB01NTR0dHYODg9++fVtYWCiUQGSxWOXl5eHh4UZGRnhk8eLFTCYzPj5eMMOVk5NTXFyMEAoJCSFmxtnZ2Q0fPjwiImLAYpYqjLq6umvXriGEBOu9WFhYuLm5HTp06PHjx5cvX542bZq6unqvQXp4eDx69Cg3N/fo0aMIIW1t7U2bNtnZ2Un4Ho2NjSkUSmNj44sXL4gEYkFBAULIwMCgurq6oKBgzpw5ePzFixcIIXV1dXNzc+IIjY2NTU1NGzdunDp1qiSzJiUh848eAAD6C0meRFLs73mGfUIi9/GfaH19/ZKSEtHsHh6RNrsnJye3bNmygoKC/fv3R0VFEQnEfjzFRwgSiL2AGjdQ4wZq3AAAAAAAYzKZHh4eCKGJEycKjpPJZHNz87dv37a0tIi+ytPTk0ghYePHj4+Pj8dTErDY2Fg8LrSudsaMGbdu3Xr79u3AxCxVGCkpKTwez8jIaP78+UJHXrduXXZ2dnt7+/PnzydNmtRrkPX19cTaLmVl5SNHjkh4Z4iRSCR7e/ukpKS///7b398fIcTn81++fIkQ8vPzO3HiREFBAZ/Px5lBnFgcN26cnNz/fBn28PCYNm2a5CeVxAf96N+8efPu3TuhQbiNBAB8CCQyqd+nGfaRXN8TiAih0tJSofGysjJia7eqqqrOnz9PoVCIpcoEV1dX4gjvc4rB4uP4E/CxqqurW7t27aVLl4qLixsbGzs7O3GxmNDQ0MOHD3e7vr2qqmrLli13796tra3t7OysrKxMTExcv369UMfumpqaoKCgmJiYd+/edXZ24kInmzZtwo8fByxmqcLg8XiRkZF79+7NzMxsamrq6urCBW62b99+8eJF8av9CbjGDYfDuXTpEkKIw+EcPXpU2ho3CCFc44YYFKpxQ4yLr3GTk5PTUxkCXOMGT5NUUVHR0dGRpMaNbD96AAAAAHxo+vr6mzdv3rx5s5KSkuA4m83GD0S7JboeVnRS3ps3bxBCU6dOFRonkUhTpkzpc8D/j707j4u62v8Hfj4z7AEDIouOQCAgQsSiaUqAmkFd0yhyuVe89/qNvpqR29WKrL6kpaYkqLev3ixLDTUxLS0V3FNcUFBAR8Bhc2QdFgcQh20+vz/OvfOb77A4zIDzgXk9H/wB57OcNx+MDu/zOe9Dehlzr8IoLS0lhAQGBqpl4gghAoHAzc2NEFJQUPDYCK9cuRIbG5ubm+vo6Mjj8eRyeXJysvJoTk7OtWvXGhoaer4JHSKKRCI6RJRKpXV1dba2tmFhYQzDNDQ0lJeXE5XEour6ZUrH59ylfv3Rm5mZCTpBAhEA+gWfR4y58aFtApH+Rj116pRa+8mTJwkhdKatS+bm5nFxcYsXL25vb1c7RMvmCoVCHbsYKPAGYk9Q40YVatz0gAs/egAAAHhi5HJ5cXFxRUVFRUXF3bt3b9261UNx9MeuWmpsbHz48CHp5vWEvnpn4bEx9zaMiooKQsjRo0ePHj3aXadNTU09R5Wenr5x40aFQjFp0qRFixalpqZ+9913aWlpnp6eERERLMt+/vnncrl827ZtdPFHd2gC8dGjR8XFxR4eHjRLOHr0aAsLi5EjR4rF4tu3bwuFQolEQt+47Dx13XnViO769Uc/bNiwYcOGqTXW1dXRxC4AQF/iM4wxJ94/Y7SNYs6cOcuXLz99+nR1dbWDgwNtLCoqysjIEAgEyjIXndnY2IwcObKwsPDXX3+lL7lTLMvSOh7K/6Fo3cVAgQRit1DjRhVq3PRM7z/6XmlsbOw8eQIwyDz2XRUAAO1UVlbu2rUrIyOjra1N2ejk5MSyrNqyA6Wnnnqq53sqd+m1sbHpfLTLxl7RMObehiGVSgkhPB6vc5EcpZ43nXz48OHWrVsVCkVERMSiRYsYhpkxY0Z+fv7Fixf/9a9/ubm5WVtby+VyPp//2EycjY3N008/XVJScuvWLQ8PDzoAGz16NCHE19dXLBaLRKLw8HA6OHR2du6cLuw5Qakdvf/oAQD6BMNnGKO++dtZV9r+CW9vbz9jxoxDhw7NnTv34MGDAoFAKpVGRUUpFIro6GjV95nWrl3b2to6ZcoUZVLlvffeW7p06aJFi4yNjf/0pz8ZGRnV1NSsXr366NGjVlZWsbGxve1igEICsVuocaPaiBo3j6XfH32vREZGKis8AgAAgOYqKytXrFjR0NBgYWExefJkT09PV1dXoVBoZWWVlJTUXQLxsZR5IplMpnxnQalXMyL0dTbtYu5tGHRONzo6+s0339Q8QlV//PFHc3Ozra1tTEwMHbwxDLN48eLS0lKJRLJ+/Xq6UsTHx6eHHKVSYGAgTSBGRkYq30Ckl//66690WNjd+mVCSOeF2E9AH/7oAQD6D8OZNxCJtpuoEEK2bNly9erVU6dO2dvbe3l50dSBr6/vF198oXra2rVrHz58aGRkpEwgxsbGHjly5MyZM6+99pqRkZGNjQ2d/rGwsPj222/pTg+96mKA4sa/AE5CjRtVqHHzWPr90QNAl+bPn//hhx/qOwoAGDxSUlIaGhqEQuGOHTtiY2MjIiK8vb3p//F1ebvfxsaGlq7rMgXZq7xkZWWl1jH3Ngy6frbzPh6ao4ugvb29VceuZmZmH330kbm5eU1NzfHjxwkhdKO5x1IOEZubm4uLi01MTEaOHEkI8fHxIYRUVlbSJSykmwSiXvThjx4AoB8ZMYwxjxMf2tZAJIQIhcKsrKyFCxc6ODgUFBS4urquXLny8uXLj93wgM/nnzx5cseOHaGhoQ4ODo8ePQoMDHz77bfv3Lkza9asPuliQMAbiI+HGjcENW40wJEfvSa8vLxkMtkT6w5Aj958880//elP+o4CAAYPOl0aGhqqNk3Isizdt007DMM4OzvfvXv33LlzL7zwgtqdL1682OVVLS0tra2tJiYmyhaFQpGVlaV1zL0Ng5aNv3nzplwuV9u74+HDh6tWrWpvb1+6dKmHh0d33zjdsK7z5LFQKFy6dOm6desIIfb29mPGjOnuDqp8fX2NjY2bmprS0tJYlvX09KTvLQoEAmdnZ4lEcv78ealUamRk5Ovrq8kNnwDtfvQAAE8Yw+dx5Q1E3aJwcHDYtm3btm3bejiny8wGj8ejOyX0SRcDFBKIPUGNGyXUuHksvf/oNTcof5cBAAA8AbSAER0Xqdq/fz8daNHlEVqYMWPGV199lZGRkZ2drVob+o8//hCLxWon02EDy7I3btxQLVxz+vRpiUSiS8y9CiM0NPTAgQN1dXX79u37+9//riwtzbJscnJyUVGRra2tah3qzlxcXAgh+fn5ZWVlyl0sKTos7OjokEqlFy9eDAkJ6eE+lImJiY+PT3Z29i+//EL+MzikfH19JRIJbffx8dF9q2KFQqHjHZR69cwBAPSC4TOMEScSiH21jQFoAQnEbqHGjSrUuNEdatwAAAAMdKNGjcrLyzt9+rStrW1QUBCfzy8rKzt58qRIJGIYhmXZzMzMrKwsb29vCwuLXt05JCTk4MGDpaWl8fHxM2fOfPbZZ+ndDh8+LBQKy8rKiMoAZsSIERYWFs3Nzf/85z/lcvmzzz4rk8nS09MPHDhAJ1m1jrlXYbi4uISHh6elpR0+fFgikbz00kvDhg2rr69PS0tLT08nhMybN6/nQZefn5+ZmZlcLl+/fv3777/v7OxMCGlra9u3b9/BgwcJIVZWVo2NjVu3bnV3d1fLMHYpMDAwOzu7rq6O/GflMuXj43PixAnaruP6ZTqOvXXrVm1trbGxse7T0r165gAA+mHEMCbc+EWkQw1E0BESiN1S1ovZsGGD2ooP3WvcyOXyqqoqT09PtaN9VePmsTH3Noxhw4YVFRX1U42b5cuXa1Hj5vDhwwOxxk2f/OgBAABAL2bNmpWenl5TU5OSkpKSkqJsHzduXGhoaEJCQl1dXXx8/Jo1a1RfJdMEj8dbuXLlqlWrZDLZ/v379+/fT9uHDx/+ySefLFy40MzMTLlamc/nL1y4cNOmTTKZ7KuvvlLexN3dPTY2dvny5VrH3KswCCH//d///eDBg4yMjOvXr1+/fl3ZzjDMrFmzpk6d2vN3bWdnN2/evB07dpSWli5evNjNzc3KykosFtMqNNOnT4+KilqyZIlMJvvyyy8TEhJUu+6SauGaUaNGKT9XXbOs4+DQxcXl5s2bubm58+fP9/LySkhI0OVupJc/egAA/WAYokPxwb7EjSgMEzdSyJzUrzVuCCHnzp1TO/TYGjeqLX1S40bzMFRr3Kgdevjw4dKlS2NjY3teZNFzjRv6eV/VuCGEcLbGDenljx4AAAC4g67qmDFjhrOzs4mJibW19dixYz/66KNVq1aFhITMmjXLwsLC29vbzs5Oi5u7uLj885//nD59ur29vZGRkb29fWRk5KZNm+jbZ2rVTiZNmrRu3brAwEBbW1sTExOhUPjnP/95w4YNnWuq9DbmXoVhYmKyatWqlStXBgUFWVlZGRsbOzs7h4SEJCYmzp07V5Pv+tVXX42NjbWwsOjo6BCLxTdu3GhsbLSwsFi8eHFMTMyQIUOWLFlCCCkpKfnmm28eezc3NzdaqN7Z2Vl1PGxvb0/Xfyh3/9Pa7NmzJ0yYYGFhYWZmZm9vr8utlHr1zAEAnjyGYRgejxMfWMKsP3gDsVuocaMKNW76BGrcAHCfTCZ7bEnTvqLL++wA8AS4u7sfOXJErdHa2rq7GurR0dHR0dHKL4cNG9b5cmrUqFFdHhIIBG+//fbbb7+t2lhcXEy6yiL5+vp+9tlnao1ddqp5zFqEwTBMSEiIJuO3LjEMEx4ePnHixMLCwtLS0o6ODicnp8DAQOUQbuzYsd09xi7vtmfPni4Pffvtt1229/Bj6vKQlZVVXFxcz6f1948eAOAJY3g8hs+N98+QQNQfJBC7hRo3BDVuUOMGwPA888wz+g4BAAzR0aNHC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Xfu3Dl58qTaZLlyz8M+n/wGAOgFhseZD30/CgOGBOLghwGiEgaIAAAAAISQUaNGEUJOnz69Z8+e27dv5+XlnT59+sMPP9y3bx99j4aOZ5qbm5WX0KUndXV15D8LUyj6OW1/7PIUQoifn5+ZmVl7e/v69eslEgltbGtr2717d2JiYkdHB53z3rp1a+cBZJcYhikuLl65cuXx48fv3Llz6tSpVatW0Qndl19+2cHBgZ42ZsyYZ599lhCyZ8+exMTEjIyMu3fvHj9+fPny5bSjt956q7fvENHzb926VVtb212lnalTp9I5+NWrV+/evTsnJ0csFh89enTNmjUsy/r6+tI1KLSwT3p6+tatW3NzcyUSyZ07d3766afNmzfTh4b1ywCgR3QXZk58DPqXPTkMNRAHv1GjRtFBoa2tbVBQEJ/PLysrO3nypEgkYhiGZVk6QPT29rawsKCXBAYGZmdndzlAPHHiRG8HiHK5fP369e+//z4dPLW1te3bt+/gwYOEECsrq8bGxq1bt7q7u2vySqBygPjaa689/fTT9BuhBRk7DxBzcnL27Nlz//794OBgW1tbsVickpJCN1rRcYBobGzc5Z7RU6dO/f333yUSyerVq994442AgAALC4s7d+7s379fbYB49OhROkCcPHmyjY1NU1NTTk7Ozz//TDBABAAAgP43a9as9PT0mpqalJSUlJQUZfu4ceNCQ0MTEhLq6uri4+PXrFnj7+9PD9FhDEXzj5TqkhRNxod2dnbz5s3bsWNHaWnp4sWL3dzcrKysxGIxnSGePn16VFTUkiVLZDLZl19+mZCQYGJi0vMNx4wZY2pqmp6evm3bNtV2Hx+fefPmKb9kGGblypXx8fGFhYVnz549e/as6qGYmJixY8c+Nng1Li4uN2/ezM3NnT9/vpeXV0JCQudzTExMPvnkk48//ri6uvrgwYN0DEw5OTktW7aMx+MRQubNm3fz5k2JRHLq1KlTp06p3sHJyWnlypW9jQ0AoC9xZxMVvIKoP0ggDn4YIGKACAAAAKCKbqZ34MCBGzduVFVVmZmZeXl5hYeHjx8/nhBy79693377zcXFxc7OTnmJm5sb3Q/E2dlZtTCOvb29g4NDdXW1QCDQcIXHq6++ampqunPnzubmZrFYTBstLCxiYmJefPFFhmGWLFmyevXqkpKSb775JjY2tue78fn8999/Py0tLTU1VSKRMAzj4uIyefLkl19+WW2bPoFAsHHjxtTU1PPnz0skErlcbmtr6+fn99prr7m7u2v46FTNnj1bKpVmZ2crFAp7e/vuTnNyctq6deuRI0fS09MrKipMTEycnZ2DgoIiIyOVo18zM7PExMTU1NQLFy5IpVKZTDZkyBBHR8eQkJAXX3zR2NhYi/AAAPoMw+NKAhFvIOoPEoiDHwaIGCACAACAIXN3dz9y5Ihao7W1dUxMTJfnR0dHR0dHqzUyDLNnz54uz//22297FQ/DMOHh4RMnTiwsLCwtLe3o6HBycgoMDFSWxxk7dmzngHu+YURERERExGPPNDIymjZt2rRp03o+rfMTGzZsWOeQrKys4uLiHnsaIcTc3Hz27NmzZ8/uoVMTE5Pp06dPnz6959gAoEtvhU54K3QCBy8ZHBiGM28gIoGoP0ggDjYYICphgAgAAADAWZaWlv7+/soVMAAAwF3cWcKMBKL+IIEITwIGiAAAAAAAAAADEsMjPP7jT3sCkEDUHyQQAQAAAAAAAACgG6iBCEggAgAAAAAAAABAt7izhBm7MOsPEogAAGAo7ty5o7oZ/UAUEBDg6emp7ygAAAAAwJAwDMONBCJeQNQjJBABAMBQHDp06NChQ/qOQieJiYlLly7VdxQAAJzwt7/97W9/+5u+o+AkPo/wuVGtbIBi+ITHEhbPUFc8i6d49o76jmKA40baDkuYgSCBCAMFBogAoDUej2djY9PQ0KBQKPQdi65WrVq1Z8+ezMxMfQcCAAAcxhLC6juGgY7HR55Cd0bunkYjXPUdxcDGXLhI2tr0HQWXljDjP0z9QQIRAAAGOYVC8eDBA0LIlClTwsPD9R2O9j766KPm5uaGhgZ9BwIAANz2sImwHfoOYiCztCZGRqS9Xd9xDHw8Hmlq1HcQAxzLjdkA7MIMSCACAIDhmDBhwgcffKDvKLT38ccfD4KXKAEAAABggGHwBiIggQgAAAAAAAAAAN3hcaYGInZh1h8kEAEAAAAAAAAAoGsM3kAEJBABAAAAAAAAAKBbqIEISCACl0ml0n379uXn58vl8pEjR86aNcvDw0PfQQEAAKQZszwAACAASURBVAAAAAAYEuzCDEggAmeJRKL4+Hi5XE6/lEqlGRkZK1aseOGFFzS/ySeffJKdnT137tzZs2f3T5gAAAAA8ORgghkAQA8YztRARAJRf5BABC5qaWlJTEyUy+VTp06Njo5mGGbv3r2pqanbt28PCgqysLDQd4AAAAAA8KRhghkAQD9QAxEI4ca/AID/q6CgoKqqSiAQLFq0aMiQIba2tgsXLnRycmpoaCgoKNB3dAAAAADwpKlOMP/www+7du2KiIhQKBTbt29vbm7Wd3QAAIMaXcLMhQ/QHzx94KKysjJCiKOjo5HRv1+S5fP5zs7OhJDy8nJ9RgYAAAAA+oAJZgAAvaGbqHDhg0EWS2+whNlAffbZZ5mZmf7+/mvWrFE2dnR0zJkzp6WlxcTE5KeffuLz//8uS6tWrcrNzfX19V23bh1taWpqOnbs2NWrV2tqapqamoYMGeLk5PTSSy8FBwerXlhRUbFgwQI7O7vvv/++oqJix44dOTk5ERERb7/9dg/hubq6EkJKSkra2tqMjY0JIW1tbUVFRYQQW1tbTb7BpKSkM2fO0M+Tk5OTk5O9vLwSEhKUJ2RlZZ04cSIvL6+pqWno0KGenp7Tp0/39vZWvUkPwT/2++ro6EhNTT1//rxEIpHL5ba2tn5+fjNmzHB3d9ewC02+TQAAAAAD0d0Ec2VlZXl5eUBAgF6jAwAY1BgeVzJ3WMGsP0ggGqiAgIDMzMy8vLyOjg5lvq+oqKilpYUQ0traWlhY6OXlRdvb29vz8/MJIUFBQbSlpqZm2bJlMplMecOqqqqqqqrs7OxLly598MEHTKfCBNXV1XFxcXV1dZqE5+HhYW1t3dDQ8OOPP86fP7+1tXXz5s21tbVWVlYajg4tLS3t7OxkMll7e7u5ubmFhYVAIKCHFArFtm3bUlNTlSdXVlZWVlZeuHBh5syZtOSi5sF3eaihoSE+Pl4sFitbpFLpmTNnzp49O3/+/MjISA3vAwAAAPDEYIIZE8wAAF0bLDUQq6urP/vss6NHj1ZVVQmFwjfeeOOTTz5R5gr65FpduuA4JBANVGBgICGkpaXl7t27ylFRXl6e8gSRSKRMIBYWFra2tiqvIoQkJSXJZDIzM7OZM2f6+fmZm5tXVVUdO3YsKyvr0qVL165dGzdunGp3CoVi8+bNdXV1Xl5e7u7uzz33XM/hGRsbx8TEbNq06fDhw66urseOHSsoKODz+UuWLDE3N9fkG4yJiYmJiaFFst944w3VItn79u2j2cMJEyZMmjTJwcFBIpEcOXJELBanpKRYW1u/9tprGgbf5SGWZTdu3Eizh2FhYcHBwXZ2dmKx+Oeff66urt65c+eIESPGjh2ry/MBAAAA6HOYYMYEMwBA1+gSZk7QPo9ZVlb2/PPP379/nxBiaWlZXFz81Vdf/f7775cuXXrsRJSG1+rSBfchgWignJ2dbW1t6+vrb9++rUwgikQiQoiTk1NlZaVIJFKOY27fvk0IsbKyorOjcrk8NzeXEBIbGxsaGkrPcXV1HTNmzJIlS+7du5efn6+WQKyvr3/w4MHSpUsnT57cefjVpbCwsPPnz2dmZiYlJRFChgwZsmzZMn9/fx2/8ZqampSUFEKI6tZ7I0eODAkJ2bBhw+XLl/fu3TtlyhQrKytNgu/yUFZWVnZ2Nu1i1qxZtN3T03PixIlxcXH379///vvvx4wZo3orLZ6PLn777TeUkgSDkpWVpe8QAAAGAEwwE0wwAwB0icedNxC1v/S99967f/9+QEBAcnLy6NGjb9y4ERUVlZeXFxcXt3379j65VpcuuA8JRAPFMExgYOCZM2du3boVFRVFCGFZ9s6dO4SQ119/fdu2bSKRiGVZmsyiicWAgAAej0cIkclkYWFhhJDx48er3pPP57u7u9+7d6+xsbFzj2FhYVOmTNE8wtraWjoqJYSYmZklJib2ScL+3LlzCoVixIgRM2fOVG3n8/mLFi26du3ao0ePcnNzJ06cqGHwnQ9duHCBEDJ06NCoqCjVbKBAIJgzZ05CQoJEIikpKXFzc9Owiz6XmJioXL8DAAAAQGGC2ZAnmAEAesJwZgdkbX8fSqXSo0ePmpubHzp0iP4xHhQUdOjQoaCgoP379yclJZmZmel4rS5dDAjc+BcA+kDXeohEoo6ODkKIVCqtq6uztbUNCwtjGKahoYG+pKZMLCqnlx0dHZcvX758+XJTU1PVG8rlclqGpkuTJk3SPLYrV67Exsbm5uY6OjryeDy5XJ6cnKw8mpOTc+3atYaGBs1vqFRaWkoICQwM5HX69ScQCOh/5J038ush+M6H6OvKoaGhygrfSsHBwSYmJqSrvaR79XwAQAvl5eWff/65vqMAAOAuOsFMCLl16xZtUZ1gJoTQCWZ6qMsJ5kmTJnU5wUwI6WGCWfPsWOcJZt2zh+RxE8xGRkZ0glnz4Dsf6nmCmRBCJ5g174IQUlxcfKGTwsJCzb9xAAANsQxhGYYLH1q/grh///729vbJkyervsoTGBgYGBgok8l+//133a/VpYsBAW8gGi6aQHz06FFxcbGHhwcdHY4ePdrCwmLkyJFisfj27dtCoVAikdABX+fiMnK5vLi4uKKioqKi4u7du7du3VIO6TobOnSohoGlp6dv3LhRoVBMmjRp0aJFqamp3333XVpamqenZ0REBMuyn3/+uVwu37Ztm7W1dW+/64qKCkLI0aNHjx492t05TU1Nmgff+VBVVRUhxMnJqfPJfD7f3t6+rKyMnqNhF31u2bJlqmt2AAzE4ChdDADQrwICAs6cOUMnmPl8vuoE8/bt2+kEs1Ao7G6CufMN+3aCOSkpqbm52dHRUSqV0gnm2NhYejQnJ6elpWXUqFFajA8fO8F89+7dgoICtRUqfTjBvGXLltbW1vLycrUVKj0/HzMzs87/a8O7igDQPxiWM/sfaxfH2bNnCSERERFq7RERETdu3Dh9+jRdmqnLtbp0MSAggWi4bGxsnn766ZKSklu3bnl4eNACN6NHjyaE+Pr6isVikUgUHh5O16c4OzurZrgqKyt37dqVkZHR1tambHRycmJZtnNqjNJwMPfw4cOtW7cqFIqIiIhFixYxDDNjxoz8/PyLFy/+61//cnNzs7a2lsvlfD6/ywzdY0mlUkIIj8dT3QpQTec0aA/Bqx3q6OigtcNtbGy6PN/GxqasrIyGoWEXfe7VV199Yn0BAADAAIIJ5u7O4eAE87Bhw4YNG6bWWFdXR/OhAAB9iOVMAlHrIOjvWE9PT7V2Dw8P5VEdr9WliwEBCUSDFhgYSBOIkZGRygEiIcTHx+fXX3+lK1PUppcJIZWVlStWrGhoaLCwsJg8ebKnp6erq6tQKLSyskpKSuruv4rOM7pd+uOPP5qbm21tbWNiYugMKsMwixcvLi0tlUgk69evpwWkfXx8esgA9oBW9omOjn7zzTc1v6qH4NUO8fl8gUAgk8kePHjQ5fk0vdi5nqOGzwcAAACg/2CCubtzBv0EMwBAD1hCWH3HQLHa5hCrq6tJV7+H6d/m9KiO1+rSxYCABKJBCwgIOHz4sEgkam5uLi4uNjExGTlyJCHEx8eHEFJZWUmraJP/m0BMSUlpaGgQCoUbNmxQLSZNCGlvb9cxJDoD7O3trVpg0czM7KOPPlq+fHlNTc3x48fJfwrxaGHYsGFFRUV0FUk/cXR0lMlkXQ6UFQoF/a3ReboYAAAAgAswwawhTDADgEFhu/uNxPZDarGHagzaFmqgf4l3/k2reQLxsdfq0sWAgP8nGTRfX19jY+Ompqa0tDSWZT09PemoSyAQODs7E0LOnz8vlUqNjIx8fX2VV9E9RkJDQ9WyhyzL5ufn6xgSrQvTeYMUoVC4dOlS+rm9vf2YMWO0u79QKCSE3Lx5Uy6Xqx16+PDh0qVLY2NjxWKxdjenhg8fTgi5cOEC3Z1GVXp6Op2+pmEAAAAAcI1ynz3tJph37NgRGxsbERHh7e1Nx4r9OsFsbm7eJxPM5D9lCvuJo6Mj6Wb9GiaYAYD7GCNjYzOzLj94xiZ0gXMffvBNTLvrjtF2ZoXtMdHZQ7UNza/VpYsBAQlEg2ZiYkLHgr/88gv5z/QyRTOGtN3Hx0d1u3Fzc3Pyn7Ueqvbv309HRZ0TZ5pzcXEhhOTn55eVlakdoitTaNcXL17U/J4KhUL5eWhoKCGkrq5u3759qv95syybnJxcVFTU2NhI9wrUGu2iurr60KFDqu0PHjzYu3cvIUQoFKpVyAYAAADgCEwwq8IEMwAAIaSjvaPlUUuXH+1t7X2eQGxrae2uO4VCyxce6URO5zfB6+vrSTc1ant7rS5dDAhIIBo6OnVcV1dH/jOxTNHPabvq9DIhZNSoUYSQ06dP79mz5/bt23l5eadPn/7www/37dtHF5VkZmZmZWU1NzdrEY+fn5+ZmVl7e/v69eslEgltbGtr2717d2JiYkdHBx2Vbt26tXOGsTMaz61bt2pra+mg08XFJTw8nBBy+PDhNWvWXL58uaSk5MaNGxs2bPjtt98IIfPmzdNxtciYMWOeffZZQsiePXsSExMzMjLu3r17/Pjx5cuX05jfeustbJAHAAAA3IQJZmU7JpgBAP6NISzDcOFD6++gu+webdEugah2rS5dDAiogWjoVPfOo5lBSnVKWS2BOGvWrPT09JqampSUlJSUFGX7uHHjQkNDExIS6urq4uPj16xZ4+/v39t47Ozs5s2bt2PHjtLS0sWLF7u5uVlZWYnFYrrT3/Tp06OiopYsWSKTyb788suEhAQTE5Me7ubi4nLz5s3c3Nz58+d7eXklJCQQQv77v//7wYMHGRkZ169fv379uvJkhmFmzZo1derU3sashmGYlStXxsfHFxYWnj17lm7lrjwUExMzduxYHbsAAAAA6D+BgYHZ2dldTjCfOHGiuwlmOqlsa2sbFBTE5/PLyspOnjwpEokYhmFZlk4we3t7W1hY9DYeOsEsl8vXr1///vvv0xch29ra9u3bd/DgQUKIlZVVY2Pj1q1b3d3dH/sen+oEs7GxsbW1NZ1gTktLO3z4sEQieemll4YNG1ZfX5+Wlpaenk76boI5Jydnz5499+/fDw4OtrW1FYvFKSkpNTU1BBPMAMBtnNpERTs0u1dYWKjWXlJSojyq47W6dDEgIIFo6Nzc3GhRZ2dnZ9UlJ/b29g4ODtXV1QKBQG06lBbDPnDgwI0bN6qqqszMzLy8vMLDw8ePH08IuXfv3m+//ebi4mJnZ6ddSK+++qqpqenOnTubm5uVq0UsLCxiYmJefPFFhmGWLFmyevXqkpKSb775JjY2todbzZ49WyqVZmdnKxQKe3t72mhiYrJq1aqLFy+ePn367t27crncycnp6aefjoqK0nFuWUkgEGzcuDE1NfX8+fMSiUQul9va2vr5+b322mt91QUAAABAP8EEs/JkTDADABBC6MpifUdBaRnGpEmTDh06dOrUqXfffVe1/eTJk4SQsLAw3a/VpYsBAQlEQ8cwzJ49e7o89O2333Z3lbW1dUxMTJeHoqOjo6OjlV8OGzbsyJEjvQ0pPDx84sSJhYWFpaWlHR0dTk5OgYGBymUyY8eO1fCeVlZWcXFxXXYREhISEhLS8+U9BP/Y78vIyGjatGnTpk3TugsAAAAAvcAEMyaYAQD+L64kELV+A3HOnDnLly8/ffp0dXW1g4MDbSwqKsrIyBAIBJGRkbpfq0sXAwISiMBRlpaW/v7+WsxRAwAAAIAuMMHcA0wwA4AB4s4SZq3Z29vPmDHj0KFDc+fOPXjwoEAgkEqlUVFRCoUiOjqaVvKl1q5d29raOmXKFFq+VvNrNe9igEICEQAAAAAABgBMMAMA6AXLEm13P+5jukSxZcuWq1evnjp1yt7e3svLSyQSsSzr6+v7xRdfqJ62du3ahw8fGhkZKROIml+r4WkDFHZhBgAAAAAAAACArilYVqHgxAerQwZRKBRmZWUtXLjQwcGhoKDA1dV15cqVly9fFggEfXWtLl1wH95ABAAAGEjq6+u//PJLLS58/vnnB0HxZgAAAAB40lhWp9RdX9IpDAcHh23btm3btq2Hc5qamrS+VvPTBiIkEAEAAAYGGxubxsbG2traDz/8UIvLly1b5u/vb2Nj0+eBAQAAAMAgpiCcWcLMjTAME5YwAwAADAwPHjxoaWnR+vLExEQXF5c+jAcAAAAADAGrIHpfvKz7EmbQEd5ABAAAGEicnJy2bNnS26sWLlxYV1fXH/EAAADn2AwhVlb6DmIg4/EJjyHGSFTohiGEMMTYRN9xDHA8Trz1xRJWwY3UHTvgt4MewJBABAAAGEgsLS1nzpzZ26uWLVvWH8EAAAAHNbSwHUh+6cDMlDVvayUd7foOZODj8Ulz1+XkQFOsQt8REEL+/fqhvqMghOhYAhF0ggQiAAAAAAAAAAB0jeVODUR9B2DIkEAEAAAAAAAAAICusSzhSPVBjoRhmJBABAAAAAAAAACArrEsljADEojwOFKpdN++ffn5+XK5fOTIkbNmzfLw8ND88h07dhw9ejQkJGTlypX9F2QPzp49u3v3bkLIW2+99cILL+glBgAAAAAAAIABimWxhBmQQIQeiUSi+Ph4uVxOv5RKpRkZGStWrBhAmTi5XF5bW0sIaWlp0XcsAAAAAAAAAAOMguXOLsygN0ggQrdaWloSExPlcvnUqVOjo6MZhtm7d29qaur27duDgoIsLCz0HSAAAAAAAAAA9C+WJRxZwowaiHqEBCJ0q6CgoKqqSiAQLFq0yMjIiBCycOHC7OzsysrKgoKCgIAAfQeokVdeeeWVV17RdxQAAAAAgwdK3AAAGBSWO28gciIKA4UEInSrrKyMEOLo6Eizh4QQPp/v7OxcWVlZXl4+UBKIAAAAANCHUOIGAMDQsCxhFfoOAvQNCUR1NTU1//Vf/2VlZZWcnCwWi/fv33/nzp2WlpZhw4a9+uqr4eHhDMPcuXMnJSUlPz+/tbVVKBRGRERERETweDzV+3R0dKSmpp4/f14ikcjlcltbWz8/vxkzZri7u6ueVlFRsWDBAtpdfX39gQMHrl27Vl9fb29v7+Pj85e//GXo0KGdg8zKyjpx4kReXl5TU9PQoUM9PT2nT5/u7e2tPOHKlStr165lGGbPnj3W1tZql7/33nulpaXTpk1bsGBBD4/C1dWVEFJSUtLW1mZsbEwIaWtrKyoqIoTY2tr27rESwrLsyZMnU1NTJRIJIWTEiBFhYWF/+tOf6J2p7OzsTz75xMzM7MCBA2qXnzt3btOmTU5OTt98841qu0gk+vXXX+/duyeVSgUCwfDhw6dNmzZ+/HiGYegJ9AkTQn788Uf6KPrpmWseUq9OAwAAAOAOlLgBADBAXHoDkRNhGCYkELt1/fr1tWvXtre30y9LS0u//vrrxsbGIUOGbN68WfmvtqioaNu2bbW1tdHR0cprGxoa4uPjxWKxskUqlZ45c+bs2bPz58+PjIzs3F1FRcWqVatqamrol+Xl5eXl5ZcuXdq8ebOjo6PyNIVCsW3bttTUVGVLZWVlZWXlhQsXZs6cSYdxhJDAwEATE5PW1tbr169PmTJFtSOJRFJaWkoICQ0N7fkJeHh4WFtbNzQ0/Pjjj/Pnz29tbd28eXNtba2VlVVvXz9kWXbz5s1nzpxRtojFYrFYfO7cuc8++6xzilNDP/zww6FDh5RfSqVSqVSanZ398ssvL1q06LGX9+0z71VIOkYOAAAAoBcocQMAYIAUhHAlgajvAAwZEohdk8vlGzdudHNz+/vf/+7i4pKfn//111/X19cnJyezLDt8+PCYmBgPD4+SkpKvv/66srLy0KFDM2fONDU1JYSwLLtx40aaPQwLCwsODrazsxOLxT///HN1dfXOnTtHjBgxduxY1e46Ojo2btz46NGj9957z9/fX6FQnD17dv/+/c3NzT/88MMHH3ygPHPfvn00kzVhwoRJkyY5ODhIJJIjR46IxeKUlBRra+vXXnuNEGJqahoUFHTlypWrV6+qJRDT09MJIfb29l2+PafK2Ng4JiZm06ZNhw8fdnV1PXbsWEFBAZ/PX7Jkibm5ea+eZ2Zm5qNHj5ydnd988013d/f79+8fP348JyensLBw586dS5cu7dXdqOvXr9Mc3NixY6dNmzZ06NDa2tojR47QVwWDg4P9/f17uLzPn7nmIekYOQAAAIC+oMQNAIABYhUsZzZR0XcEBgwJxK61tbU5ODisW7fOxMSEEDJu3LjW1tYNGzZ0dHTY2NgkJCQ89dRThBB/f//33ntv1apV7e3tFRUVTz/9NCEkKysrOzubEDJ37txZs2bR19M8PT0nTpwYFxd3//7977//fsyYMaqvrTU3N5eWlm7evHnEiBG05c9//rNMJjt27FheXp7ytJqampSUFHrn2bNn08aRI0eGhIRs2LDh8uXLe/funTJlipWVFSHk+eefv3LlSlZWVmtrK/0uKJpADAkJ0WSpbFhY2Pnz5zMzM5OSkgghQ4YMWbZsmRbprUePHrm5uX3xxReWlpaEEFdX1+eff37z5s3nzp07e/ZsVFSUs7Nzb+95/fp1QsjIkSM//vhjun7c1dXVz89vwYIFNTU1OTk5PcfZH89cw5B0jFx3O3bsKCws7NcuAJ6Ad999V4tfHQAAWkCJGyWUuFGFEjcAYCBYgiXMgARi92bOnKmad/P19aWfvP766zR7SPn4+PB4PIVCIZPJaMuFCxcIIUOHDo2KilL9n71AIJgzZ05CQoJEIikpKXFzc1PtLiIiQpnJosaOHXvs2LH6+nply7lz5xQKxYgRI2bOnKl6Jp/PX7Ro0bVr1x49epSbmztx4kRCyHPPPcfj8VpaWrKzs5977jl6pubrl6na2trW1lb6uZmZWWJiohZDQyo6OppmD5Uxv/XWW5cuXWptbU1NTY2JientDaurqwkhI0aMUB2aGxsbx8bG1tTUODk5PfYOff7MNQxJ98h1tH//ftXl5AAD1Lhx40xNTR0cHPQdCAAYEJS4QYkbCiVuAMCgsApsogJIIHZv5MiRql8qk4YeHh6q7Xw+38zMrLm5Wdly//59QkhoaKhyZYdScHDwli1bWltby8vL1RKIyhyfEn2pTRUd2AUGBqrNZhNCBAKBm5vb3bt3CwoKaDLLysrKz88vOzv76tWrypvT1w+FQqFa7126cuVKUlJSc3Ozo6OjVCqVy+XJycmxsbH0aE5OTktLy6hRozQZ3gkEArVV27Rx/PjxFy5cKC8vf+wdOhMKhdevX7906dIzzzwTFhZmZmZG24OCgjS8Q58/cw1D0j1yACCEREVFzZs3b/fu3foOBAAMBUrcEJS4+Q+UuAEAg8JypwYiJ6IwUEggdkv1NUNN2pWqqqoIIV2+R8bn8+3t7cvKyug5qjR576yiooIQcvTo0aNHj3Z3TlNTk/Lz559/Pjs7OyMjg2VZOgtKR4ehoaGPXQeRnp6+ceNGhUIxadKkRYsWpaamfvfdd2lpaZ6enhEREez/Y+/O45q60v+BnxBAQJF9M7KKSLWIoCCI4NIqOtaVqm3Fn3XE0jq4tmo7amvbGbVqXeuXWls3ijrg0hG1iuIOBQQUqMgmghHZl7AZAsn9/XG+c7+ZBDBAIAE+7xd/hHOX8+TeVq/PPec5DPOPf/xDKBSGhoYqkkC0sLBosUcej0cIKS4ufu0Z5M2dO/fmzZu1tbWHDh06duyYp6fniBEjXF1dFR/Bp/RrrmBInY+8k3bt2lVZWdk9fQF0haysLPZlBgBAt0GJGwolbtSkxE1TU1NTU5NMIzt5CABAiSTqswozllFRHSQQlUwsFtO5zIaGhi3uYGhoWFhYWFZWJtP+2rwkIYQepaGhweVyW9tH+qHBy8vr8OHD1dXVWVlZzs7Oik9Oqa+vP3jwoEQi8ff3X7FiBYfDmTVrVlZW1v379w8fPmxvbz9w4EChUMjlchXMebU28ZlepZqaGkVOIsPY2PiHH344fvx4bGxsQ0PD7du3b9++TQhxdHScN2+ej4/Pa5+AlX7NFQyp85F3EoY6Qk9nYGCg6hAAoI9CiRuCEjdqU+ImNzdXvqp1h6d+AwC0gWHUZREV5A9VCAlEJeNyuQYGBgKBoLq6usUd6HNkxx6zjIyMqqqqAgMD3333XUX2NzExcXJyys7OTkhIcHZ2pu+WHRwc6Li/Nty9e7ehocHIyCgoKIg+43I4nFWrVhUUFPD5/B07dtAnzuHDh7eRVpPW2tWg9WWkC8q0pr6+Xr7RyMho7dq1n3zyycOHD9PS0tLS0vh8fm5u7s6dOxcuXLho0SJFYmtbe6+5giF1Q+QAAACgdChxgxI3RG1K3NjY2JiZmck0NjQ0pKWlKfJNAQAUxzAE+UNAAlH5LCwsBAKB/CRlQohEIqFvF62srDpwZisrq7y8PPoAqiAvL6/s7Oz4+PglS5aw85dfexSdt+vs7Eyr9lA6Ojp///vf161bV15e/vvvvxNC5s6dq2AYRUVF7DRqaYWFhUSxqcRtTHPW0dHx9vb29vYmhOTm5p48efLRo0cRERFz5sxRZIxh2zpwzRUPqUsjBwAAAKVDiRuUuCFqU+Kmf//+8v/hoUwNAHQFCaM+NRDVIoy+SfalGXTeoEGDCCH37t0Ti8Uym2JjY+mMj9eOAWwRPerRo0dCoVBmU319/Zo1a0JCQqSX9iOE0ORUYWFhQkICfVnq6+v72o7ou3H5mcU8Ho8taG1mZjZ69GgFI6+pqUlISJBpLC0tTUxMJIQ4ODhItzc2NspUb5FIJCkpKdItTU1Nn3/++caNG2XaHR0dP/nkE0IIwzD02a6TFL/mCobUbZEDAACAmlCkxA35T+EUae0qt6LVOpkSN4QQWuKGtGf+snSJm7Vr1+ro6MyaNWv8+PGEkMOHD2dnZ5eUlKhJiZtJkyZpa2vTQjGHDh366KOP1q1bd//+zHDCHgAAIABJREFUfUX+2an0a65gSJ2PHACgCzEMI1GPH/xxqDoYgah8fn5+t2/fLi0tpUvvse3V1dWnTp0iCs8QafHMERERlZWVp0+f/vDDD9l3tgzDhIeH5+XlGRkZySTjeDyetbU1n88PDQ0lhLzxxhvyMx3k2djYEEKysrIKCwtlcp30uVAsFpeVld2/f1+RdCQVFhbm7OzMPjeLRKLDhw+LxWI9Pb1p06bRRrqVYZiHDx+OHTuWPTYmJobP50ufTUtLq7Kysri4+Pr1625ubtKvr/Py8uiHjg3zlKH4NVcwpG6LHAAAANQESty0CCVuUOIGAHoKidpMYQYVQgJR+UaPHj1y5Mi0tLSwsLAXL174+PgYGRnl5uZGRkbS56Fly5Z1bJUMGxubqVOnRkdHX7hwgc/nT5kyxcrKqqqqKjo6mj78LV68WL4Ui5eXF5/Pp9MZFKyN7eLioqOjIxQKd+zYsWHDBroEXlNT0+nTp8+ePUsI0dfXr62tPXjwoCKPm4QQQ0NDPp+/YcOG2bNn29vb8/n8a9eu0YF70kXHBw8erKen19DQ8MMPPwiFwpEjRwoEgtjY2IiICDs7u/z8fOlzenh4REVFxcbGHjx4cNKkSYaGhnV1dWlpaefOnaNfQSmzgNt1zRUMqXsiBwAAAPWBEjctnhAlblDiBgB6BIaozSrMahFFH4UEovJxOJz169dv3br16dOnt27dunXrlvSmoKAg+XLRivvoo4+qq6sTExOTkpKSkpKkz7xgwYK3335b/hBvb+/IyEi6j4+PjyK9mJiYLF68+MiRIwUFBatWrbK3t9fX18/Nza2trSWEzJw5MyAgYPXq1QKB4Lvvvtu9e7f0coQtmjNnTmpq6sOHDw8fPizd7uPjI/3mlsvlfvzxx3v27BEIBN9//z3b7uDgEBISsm7dOuljFy9e/OjRIz6ff+PGjRs3bkhvsrS0XL9+vSLfVBGKX3MFQ+q2yAEAAEBNDBo0KDs7+969e4GBgTID9JRY4oZdeYOqr6/ftGlTc3PzmjVrpJd58fb2PnnypHJL3Gzfvp10qMQNnVLNarvEjfQzZ4slbrZs2cIwzMKFC6XXHqGFYoKDg2mhGJkVbzpA8WuuYEi2trbdEzkAQMcwEqImqzAzWEZFdVADsUsYGBjs2rUrODjY2dm5f//+XC7X1NR00qRJe/funTlzZmfOrK2tvWnTpvXr17u7u+vr62tpaVlbW/v6+u7du7e1eQ1DhgwxNTUlhLi6urZWeUfeO++8ExISoqenJxaLc3NzHz58WFtbq6ent2rVqqCgIGNj49WrVxNC8vPzf/rpp9eeTVNT88svvwwKCrKzs9PW1tbT0xsxYsSnn366YcMGmWfoiRMnbt++3c3NzcjISFtbm8fjvf/++zt37pR/46qjo7N3797ly5c7OzubmJhoamqam5u7uLisWLHi0KFDin/T11L8misYUrdFDgAAAGqCDvGjJW6k25VS4oYQQsutSJeGYsut1NbWtljihhDS4RI3MptoiRtCCC1xo3jwYWFh0hOZX1viRvrY1krcPHny5Pr16zJVspRe4oYods0VDKnbIgcA6BjJf2Yxq/wHIxBVCCMQZZmaml68eFG+XVtbu8V2QsiZM2fkGzU1NWfMmDFjxoy2u7OysmrttMOGDWtxE4fD8fX1Vbz4YFNT06tXr4jC85fZXqZOnTpu3LinT58WFBSIxWJLS0s3Nzf2LeuYMWNai1za8uXLly9fTj/PmjVr1qxZrz1kxIgRX3/9tUxjixdKW1t75syZbedk5Q/s0muuSEiK7wYAAAC9A0rcyEOJG4ISNwDQQ9A1VFQdBSEE4w9VCQnE3i85Obm+vl5TU5PWUmmXAQMGuLq6urq6dkVgAAAAAH0EStzIQ4kblLgBgJ6CYRhGPaYwYwiiCiGB2MsxDHPp0iVCyOjRo/HeEgAAAEBVaImba9eu3blzh8/nC4VCIyMjFxeX2bNny0wxbi9abuX+/fsxMTE5OTlCodDS0tLOzi4gIKC1M9MSN+Xl5e0tcdOvX7+jR482NDTQoYKEED09vaCgoLfeeovD4axevfqbb76hJW5CQkLaPhstcXP58uUbN268fPlSU1PT3t5+2rRpfn5+MoMxJ06caGZmFhERkZ+fX19fb2Zm5ufnFxAQUFFRIXNOWijm2rVr9+7dKysrEwgExsbGFhYWvr6+b731lpaWloLf9LUUv+YKhtRtkQMAdADDEIxABA6jHv8RgNJlZGRUVVWlpqZevXqVEPLNN9+MGjVK1UFBx4lEoujoaHd390GDBqk6FgAVe/DggaenJyFk8eLFJ0+eVHU43UdLS6u5udnR0TEnJ6e9xw4ePLiwsFBfX19+AQRol6ysLGdnZ0LI7Nmzf/vtty7t6/jx40uXLiWEbN++/fPPP+/wecRiMV0ed+TIkbSMHYBIJFqyZEl9ff2qVataHKLYhrq6utZK3IDKVVZWxsXFjRrt1X+Avqpj6cF0+nF1JU1E3KzqQHo+DS5pqFN1ED1bdFKKqKnZycnJyclJhWE8zKkoqmxQYQCsEXZGthYDVB1FH4URiL1WbGxsVFQU/ezh4YFpyADQ+8THxwcHB6s6iu4jkUhUHQIA9BIocQMAAIqTENRABCQQey9ra2sjIyOxWOzr67tkyZKOleUGAFBDGhoab7zxRn5+fk5OTgfG4vVo1tbWb775pqqjAICeDSVuAACgXRgJI0ENxD4PCcRea9q0adOmTVN1FAAAyieRSJ48eaLqKFRj3bp1a9asUXUUANBTsSVu0tPTCSEzZsxQdUQAANADMIQgfwhIIAIAQE/19ttvb9y4UdVRdCvVlr8BgJ4OJW4AAKADJFhEBZBABACAnsvKyqq9tf8BAPoylLgBAICOYBhGPYYgYh1gFUICEQAAAACgT0CJmz6inxZXR5ur6ih6MC2uBhEzjFis6kB6OA6R6A5o5vZTdRw9G8PRUHUIhPzvCERVB9GVSktLv/7666ioqJKSEh6PN2/evC1bthgYGLTrJEePHl22bFlOTo6jo6PMptraWoFA0OJRFhYWWlpaHYy7eyGBCAAAAAAA0Hu8KK/XrlN1ED2ZmYGOyfM/mYpSVQfSw2lq1rlPrqxpVHUcPZuaLF3CMGqzCnMXRFFYWOjl5fXixQtCyIABA549e/b9999fvnw5Li7OyMhI4cCYU6dOtbZ1165d3377bYub0tPTe8oaiWqRzAYAAAAAAAAAADXEMEQiYdThh+mCKogrV6588eLFqFGjHj9+XFNTk5ycbGdnl5mZ+cUXXyh4BrFYvGfPnpiYmNZ2yMnJUVKwqoQRiL3NrVu3Tp48SQhZtmzZ+PHjVRVGWVnZ6dOns7KyhELhkCFDFixYID+IFwAAAAAAAADUnKT3jkAsKyuLiorS1dU9f/68vb09IcTd3f38+fPu7u5nzpzZt2+fjo5OG4dfvnw5MjLyzp07+fn5beyWm5tLetRgwxZhBGIPsGXLllmzZv3rX/9SZGehUFhRUVFRUdHYqLKx4hkZGX/7299u3LjB5/PLysri4+M/++yz+/fvqyoe1WrX7QMAAAAAAABQK8x/yiCq/EfpWcwzZ840NzdPmjSJZg8pNzc3Nzc3gUBw+fLltg+/cOHCiRMn2s4eMgyTm5uroaExdOhQpcSsKkgggpI1Njbu3btXKBS+/fbbx48fP3HihL+/v0Qi+fHHHxsaGlQdHQAAAAAAAAC0A62BqA4/Ss8g3rp1ixDi7+8v005b2piVTH355ZcP/6O1RVcqKyurq6sdHBz69evZawphCnNvM3369OnTp6swgOzs7JKSEgMDgxUrVmhqahJCPv7449TU1OLi4uzs7FGjRqkwNgAAAAAAAABoFwmjRsu5KPeEJSUlhBD5sYG0CBvd2gYbGxsbGxv6mSZA5NH5y87OzhEREWFhYXl5edbW1qNGjVq1atWgQYM6GX93QgIRlKywsJAQYmFhwf7Pw+Vyra2ti4uLX758iQQiAAAAQDdDjWwAAOgMhiHqkT9U/hTm0tJSQoihoaFMO11/mW7tJJpAvHr16qVLl2hLRkbGtWvXfvrpp6NHj86ZM6fzXXSPvptAbGpqunLlyt27dwsLCzU0NHg83pgxY+bMmSMzplQsFl+7du3OnTt8Pl8oFBoZGbm4uMyaNcvBwUF6t9TU1C1btujo6ERERMh0dPv27T179lhaWv7000+0paioKDg4WF9fPzw8vKqqKiIi4sGDB1VVVWZmZsOHD//ggw9MTU3pnvv27bt58yb9HB4eHh4e7uTktHv3bnoGExOTY8eOFRUVHTlyJC0tzd/ff/ny5XQTIeTXX38dOHCgdCQpKSlXr17NzMysq6szNTUdOnTozJkznZ2d5S9ORkbGv//97+fPn5eVlRkYGAwaNGjGjBljx47lcDivvbC2traEkPz8/KamJi0tLXqp8/LyyH/+D1TEiRMnzp079/7777/33nt37ty5cuVKQUEBh8MZPHjw5MmT/f39uVyuzCGK3KnDhw9fvnxZX1//5MmT8mf4+uuvk5OThw4d+v3337frorV2O+rr6+Vv37vvvrtt2zYOhxMWFiZzgwghK1euLCgomDFjBr2JAAAAAC3asmVLamrqokWLFi5c+NqdaY1sQohqa2Rv3bpVKBTSX8vKyhITEz/77DMVJjRVqF23DwBA5QbqadlZDGhxk6BeVFGr5L9ceCZ6/bRk/8FOaWkquRAfTRHKJyuUnkBsbm7+7LPPFi9ebGtrm5aW9sUXX8TGxi5evDgzM5PH43W+l27QRxOI1dXVX375pXSdy6ysrKysrOjo6F27dhkbG9PGmpqarVu30ptNlZWV3bx589atW0uXLu18nrioqGjTpk3l5eX015cvX758+TIuLm7//v0WFhaEkAEDBpiYmAgEgubmZl1dXT09PZlJ9aWlpV988UVlZWXbHUkkktDQ0GvXrrEtxcXFxcXF9+7dmz9/fmBgoHRm8Pjx4+fPn5f+ymVlZampqdOmTVuxYsVrv5Sjo+PAgQNramp+/fXXpUuXikSi/fv3V1RU6Ovrt3f4IcMwhw4dio6OZluys7Ozs7Pv3bv35Zdf6urqsu0K3ikfH5/Lly/X1tZmZmaOGDFCuq/6+vpHjx4RQiZOnEhb2nXRKJnb0eLtc3Nz09bWFolESUlJkydPlj6cz+cXFBQQQvz8/Np1oQAAAADUmXSNbPoQderUqWvXrv3444/u7u56enqqDhAAANoyUE/LWL/l+n0FpXVlAqFyuxtkojdQT7vFTVzu60c1tUvbc6JFIlHnu7C0tFy4cOHbb78dFBREW3x9fW/fvu3m5vbnn39u3br1yJEjne+lG/TFBKJEItm+fXt+fj6Hw5k7d667u7u2tvbDhw/PnTtXVla2b9++r7/+msPhMAyza9cumpOaMGGCj4+PiYlJbm7uuXPnSktLjx49Onjw4DFjxnQ4DLFYvGvXrlevXq1cudLV1VUikdy6devMmTMNDQ3Hjx/fuHEjISQoKCgoKIi+opw3b57MK0qJRLJ///7KykonJycHBwcPD4/W+jp9+jRNhHl7e0+cONHc3JzP51+8eDE3NzcyMnLgwIGzZ8+meyYlJdHs4ZgxY2bMmGFqalpRUXHx4kU6EM/Hx8fV1bXt76WlpRUUFLRnz54LFy7Y2tpeuXIlOzuby+WuXr1aOuWniFu3bpWUlJiams6ZM8fR0fHly5fXr19/8uTJ48ePT548yY7RU/xODR8+3MDAQCAQJCQkyCQQHzx4IBaLNTQ0fH1923vRKPnbMWrUqBZvn7u7e3x8fEJCgkwCMTY2lhBiZmbW4rBQAAAAgI5BjWwAAOiM/JK6p0U13dbdH09aHffn+6Zlx845b968mpr/+wq+vr5fffUVIcTCwqK2tra6ulpm/6qqKkKIpWUHu5MWHBwsP8VQU1Pz888/DwwMjI+P73wX3aMvJhBjY2OfPHlCCPn888+9vb1po7Ozs42NzXfffffo0aMXL15YW1unpKSkpqYSQhYtWrRgwQI63Gzo0KHjxo374osvXrx4cezYsdGjRysyq7dFDQ0NBQUF+/fvHzx4MG15//33BQLBlStXMjMzFTlDVVVVdXX1mjVrJk2a1EYY5eXlkZGR9IuwOawhQ4b4+vru3Lnzjz/+OHXq1OTJk/X19QkhSUlJdOvmzZs1NDQIIba2ti4uLsHBweXl5Wlpaa9NIBJCJkyYcOfOneTk5H379hFCjI2N165dq8iBMkpKSiwtLbdv325iYkIIGT58+MSJE/fs2XP//v3o6Oh3332Xtit+pzQ0NLy9va9evZqQkLB06VLpixYXF0cIGTVqFK190K6LRil4OwghXl5e8fHxKSkpIpFIW/v/3qvQBKKvr2+H/6NS0DfffPPnn392aRcAXeq1w64BAECtoEY2AECPxhBG6auXdLO7d+/Sah4UW/TQwsIiNzdXPoFIW5SSQGzNyJEjCSFZWVnNzc2tLcCiVnpAiEpH1+EeMWIEmz2kxo0bZ25uXlpampGRYW1tfe/ePUKIqalpQECAdELHwMDgvffe2717N5/Pz8/Pt7e373Ak/v7+bPaQGjNmzJUrV2iqWxETJkyQGcUm7/bt2xKJZPDgwfPnz5du53K5K1asePDgwatXr9LT08eNG0f+M8N/8ODBNHtIaWlphYSElJeXK/g/T0VFBTvQV0dHZ+/evYpXP5TxwQcf0CwhpampuXz58oSEhKampqtXry5atIgQ0q475ePjc/Xq1aKiIpompnsKhcKUlBQiNX+5XReNpcjtIIR4eHhoaGg0Njampqay40a7c/7ynTt32OKMAD2Xh4eHzFBiAIBOQo1s1MiWOQNqZAMAEEIYCZFIVB0EIYSQDqcx2dpxMmj5uKdPn8q005J3dGsXoRU8+vfvL/+3j3pScvnJHuH58+dEKlXE4nA4//jHP/bt20dzOi9evCCE+Pn5yWeCfXx86MCxly9fdiYS+UnH0iPaFCH/LeTRtJSbm5t0TpAyMDCgabXs7GzaQot3xsXFXbt2ja1yTQhxd3efOnUqTZC3LT4+PiQkJD093cLCQkNDQygUhoeHs1vT0tIePHggPXK4DTo6OuyEYpaRkZGnpyf5z30k7bxTb775Jr3ICQkJ7G7Jyckikahfv35eXl60pV0XjaXI7SCE6Ovru7i4yMRAhx/yeLzOpKQB+pSDBw/Sag8AAEpRXV396aef/vLLLzk5OQ0NDXV1dVlZWeHh4StWrJAe+FxTU7N+/foff/zxyZMndXV1zc3NtPLy2rVrf/vtt86HUVRU9Omnn16+fLm0tLSpqenly5c3btwICQkpKSmhO9Aiy/SxR1dX18TEpMUa2UlJSW1XbpJIJIcOHdq6dWt8fHx1dXVzczOt9bxhw4awsDCZkSbHjx///PPP//jjj8LCQpFIRAtkb9u2LTQ0VJEvRWtki0SiX3/9lRAiEon27dvXmRrZe/bsyczMfPXqVUNDQ3Z29o8//rh58+ZXr15J76ngnfLx8SGE0BrZMn21WCNb8YtGydyOFm8frZHNMAydDCQNNbL7gvzikn/Hxqfm5qnbIQAshjASRi1+GGWvw0z/hL9x44ZM+/Xr1wkhEyZM6OT56+rq3nzzzTfffFM+DZKVlUUIGT58eFfPQVSWPjcCsampiQ5bbXEwnXQjfUprcTcul2tmZlZYWMg+yXVM50fDsu+i21BUVEQIiYqKioqKam2furo6+mHu3Lk3b96sra09dOjQsWPHPD09R4wY4erqqmCosbGxu3btkkgkEydOXLFixbVr13755Zfo6OihQ4f6+/szDPOPf/xDKBSGhobKv1yVZ2Fh0WImnmY52YvfrjvF5XK9vLyuX7+emJj47rvv0kY6f9nb21tHR4e2tOuisRS5HZSXl1dqampiYiLDMPQPC5pA9PPz64Y/O0JDQ2tra7u6F4CuhmqhAKBEqJGNGtmokQ2qEv0g5W/7/mf5O9P+Z+3f1OoQAJaEIRL1mMKs9Cjee++9devWxcTElJaWmpub08a8vLzExEQDA4POr507YMAAS0vLmJiYn3/+ed26ddKbDh48SAhRZBajmuhzCcSKigr6bpCd8d4isVgsEAja2M3Q0LCwsLCsrKwzwfTv378zhxNCFEnD0SA1NDTaGBbLvqA2Njb+4Ycfjh8/Hhsb29DQcPv27du3bxNCHB0d582b5+Pj00Z6q76+/uDBgxKJxN/ff8WKFRwOZ9asWVlZWffv3z98+LC9vf3AgQOFQiGXy1UwHSk9eVm+nY5A7sCdGjdu3PXr17Oysqqrqw0NDUUi0YMHD8h/jx9s10VjKXI7KC8vr8OHD1dXV2dlZTk7O3fzu2UnJ6du6AUAAKAHQY1sghrZfbtGNgBAGxiGUZMEIlH2CEQzM7NZs2adP39+0aJFZ8+eNTAwKCsrCwgIkEgkgYGB0m+5tm3bJhKJJk+e3N5/tn/yyScxMTFffPGFoaHh//t//09TU7OoqGjLli1Xr17l8Xg9aE5Vn0sgsike+eFj0rhcLn0VKV9Kk6JJK0WKttTX17c/TEXJT7CVZ2RkVFVVFRgYyA64e+3+a9eu/eSTTx4+fJiWlpaWlsbn83Nzc3fu3Llw4UJadrBFd+/ebWhoMDIyCgoKoo84HA5n1apVBQUFfD5/x44d9B348OHDFZzh39pMZ1ojkj7JdeBOubq66unpNTQ0PHjwYMqUKY8ePRIKhQYGBtKPsO29aJQit4MyMTFxcnLKzs5OSEhwdnamj4YODg50cCUAAAB0M9TIplAjuwfVyK6oqJB/AG5ubn5tXwAA7cUwPb4GYhsOHDiQkJBw48YNMzMzJyenjIwMhmFGjBjxz3/+U3q3bdu21dfXa2pqtjeBGBAQsGrVqgMHDixbtiw4OHjgwIG0NMqgQYNOnz49YMAAZX6ZrtTnaiDq6enRMjEtzj4uKCiIj4+nte1oscwWd5NIJPRBysrK6rU9FhcXdzLmTqJB0kKBitPR0fH29g4ODqZVZmh5moiIiDbyoXTar7Ozs3ShcR0dnb///e+6urrl5eW///47IWTu3LkKxlBSUtJiKRla0JC9+O29U5qamrTWIS1BSN8t+/n5Sac1O3bR2oXGQJdsZ+cvd113AAAA0AbUyGahRjal/jWyKyoqcuW0tkoAAEBnqLz0oVQNROXj8XgpKSkff/yxubl5dna2ra3t+vXr//jjD5kSw52xb9++ixcvTp482cLCorGxcezYsWvWrElPT5f/G02d9bkRiISQwYMHCwSCe/fuyb8M/PHHHx8/fjx//nwnJ6dBgwZlZ2ffu3cvMDBQZsRcbGwsfYMqM16ssbFRZtKBRCKhry5ViAZJx9mxNf6o+vr6TZs2NTc3r1mzxtHRsampacuWLQzDLFy40N3dnd3N0dHxk08+CQ4OZhimqKjI0dGxxY7o85n8wx+Px1uzZs327dsJIWZmZqNHj1Yw8tra2uTkZJlCQtXV1TTpxr6c78CdGjdu3M2bNx89etTQ0JCYmEgImTRpkkzMRLGLpuB3keft7X3y5MnCwsKEhAT6PNqz/uwAAADoNVAju0WokU3Uu0a2k5OTfF2ayspKGrnKlZeVNjTIXg0TU/P+/Qe8ditAVysrLWlokB0ZY2pq3r/njAXrZgxRnxqIXRKGubl5aGho2yuDtT2NlbS+0DMhhMPhzJw5c+bMmR2MTz30xQSir6/v48ePk5OTU1JSpNNkmZmZGRkZhBBaPtnPz+/27dulpaXnz5+XnqdQXV196tQp8t8vA+l0WoZhHj58OHbsWHbnmJgYPp+vlLAlHR0x7OfnFxERUVlZefr06Q8//JB9/mAYJjw8PC8vz8jIyMHBgRCipaVVWVlZXFx8/fp1Nzc36SeVvLz/Xa6rjUGXNjY2hJCsrKzCwkKZhB0tfSgWi8vKyu7fv694piwsLGzYsGHsi/fm5uYjR46IRCIulzt9+nT2Cyp+p6hRo0bp6OgIhcKwsLC6ujoejzdkyJCOXTQFyd8+Ho9nbW3N5/PpH1JvvPGGmZmZ4icEAAAAZUGN7BahRjbpSzWylW73ti2XfouUadyx98d35ix47dZeT+utFpIIRy5dPXLpqnRL0uH9ro4O3XlI37H9203/Ph8h0/j9gSNzAha2uD8wEkYiUZMEoqoj6MP6YgLx7bffvnz5Mp/P/+abb+bNmzdq1Cg9Pb0nT56cOXOGTnSn03VHjx49cuTItLS0sLCwFy9e+Pj4GBkZ0RXW6KPJsmXL2KelwYMH07J6P/zwg1AoHDlypEAgiI2NjYiIsLOzy8/P70zAtJc///yzoqJCS0tL8acQysbGZurUqdHR0RcuXODz+VOmTLGysqqqqoqOjqZvNRcvXsxOxPDw8IiKioqNjT148OCkSZMMDQ3r6urS0tLOnTtHCHFxcWnjodbFxYVm5Xbs2LFhwwZaQaapqen06dNnz54lhOjr69fW1h48eFDBYn8cDufZs2fr16+fPXu2nZ1dYWEhXWWPEDJt2jR2gaR23SlKW1vb09Pz7t27V65cIYRMnDhRZod2XbTXfgvSyu3z8vLi8/m0/EEPfTQEAADoBVAjW5H9USMbNbIBoM+SEKIe+UNlL6EC7dEXE4ja2tpbtmzZvHlzaWnp2bNnaW6LsrS0XLt2Lf1rnsPhrF+/fuvWrU+fPr1169atW7fY3TgcTlBQkPTUWi6X+/HHH+/Zs0cgEHz//fdsu4ODQ0hIiMxa3e1lY2Pz6NGj9PT0pUuXOjk57d69u71n+Oijj6qrqxMTE5OSkug6euwXWbBgwdtvv822LF68+NGjR3w+/8aNGzdu3JA+iaWl5fr169voxcTEZPHixUeOHCkoKFi1apW9vb2+vn5ubm5tbS0hZObMmQEBAatXrxYIBN99993u3bul53q3aPTo0f369YuNjZUZSDx8+PDFixdLfwvF7xTKLUweAAAgAElEQVTLx8fn7t27dLjBhAkT5HdQ/KK1rY3b5+3tTdfy43A4Pj4+Cp4QAAAAlIvWyBYIBCUlJXQmirSCgoKioiJjY2MnJycLCwu6m/xJelyN7Ly8vI7VyKbrzOTm5p48efLRo0cRERFz5sxp7R1zGzWy161b1+Ea2fJjHuVrZLfrTtEa2Tdv3kxISJgyZUprNbI7cNHaxcvLKzs7Oz4+fsmSJT29RvaOvYd37D3csa293qvo36R/PXLp6qoDPy6b4f/D6k+k27lS2efuOaTv2HPw5z0Hf1Z1FD2JOq3CDCrTFxOIhBBLS8uDBw9evHgxNja2qKhIW1vb2tra3d19zpw50lktAwODXbt2Xbt27c6dO3w+XygUGhkZubi4zJ49W3766sSJE83MzCIiIvLz8+vr683MzPz8/AICAmhJnc5YuHBhWVlZamqqRCLp2CxXbW3tTZs23b9/PyYmJicnRygUWlpa2tnZBQQEyHwRuijetWvX7t27V1ZWJhAIjI2NLSwsfH1933rrLS0trbY7euedd/r163f06NGGhobc3FzaqKenFxQU9NZbb3E4nNWrV3/zzTf5+fk//fRTSEhI22fjcrkbNmyIjo6+du0an8/ncDg2NjaTJk2aNm2azDvqdt0pyt3dvV+/fo2Njc7Ozi1OmVH8orWtjds3ZMgQU1PT8vJyV1fXtudMAQAAQJdCjWwWamSjRjZ0Nc3//m+SpvA0OBzN1sfhds8hAK1hGKI2U5jVIoy+qY8mEAkhurq6CxcuXLjwNTUONDU1Z8yYMWPGDEXOOWLEiK+//lqm0crK6uLFi223sIYNGya/SV9f/4svvlDwDK1t4nA4vr6+ijyCaGtrd7i6J4fDmTp16rhx454+fVpQUCAWiy0tLd3c3NhnrDFjxrQWeWsn9Pf39/f3f+2e7bpThJB+/frR0X9t967IRWvjdpCWbh+rqanp1atXpCe/WwaAnqWhoWHKlCmqjqJna2hoUHUI0CVQI5u2o0Y2amQDAMiTMGozhVk9wuib+m4CEbrOgAEDXF1dpUvGQIuSk5Pr6+s1NTXpVCAAgK7j7OzMMMzLly9lylNAx7i7u+OvuV4GNbJRIxs1sgEAWqM+U5jVIoi+CglEANVgGObSpUuEkNGjR3d+vUUAgLZlZmbSAmGgFDt27MBYzl4GNbKlvwhqZBPUyAYAkMIQ9UkgqkUYfRMSiADdLSMjo6qqKjU1NT09nRCi+LRrAIBO6t+/f1pamqqj6A0UWSUDehzUyEaNbNTIBgBokURCOlozQ8nUI43ZR3FQgRLU0IkTJ86dOzd27NhNmzapOhblO3LkSFRUFP3s4eGxefNm+ZUE5YlEoujoaHd390GDBnVxgADQCw0ePLiwsFBfX19+KQNQW8ePH1+6dCkhZPv27Z9//nmHzyMWi+lCtyNHjqQF6QC6X11dXWs1shXUu58PWSKRaMmSJfX19atWrVJ8SCOrsrIyLi6O5zhKWwcTXDrOzEDH5Hk6U1Gq6kB6OE3NOvfJlTWNqo6jZ8tKixM3Nzk5OTk5OakwjF9vPU17VqnCAFhzvW293zBXdRR9FEYgAnQ3a2trIyMjsVjs6+u7ZMkSRbKHAAAAAD0aamQrCDWyAUANoQYiECQQQT0tWbJkyZIlqo6iq0ybNm3atGmqjgIAAAAA1AtqZAOAepIwjEQ9lmHGJFoVQgIRAAAAAABAlVAjGwDUGcMQ9cgfYgSiKiGBCAAAAAAAoEqxsbHSNbIx1xsA1ArDEHWZwqwWUfRRSCACAAAAAACoEmpkA4A6kxD1qYGoFmH0TUggAgAAAACAukONbMXp62rp6mkr62x9kE4/LsfYjKOlpepAejguV1tLY4AuLmOnqMnbBEZCJBJVB0Ehf6g6SCACAAAAAAD0HlpXI7RFQlVH0YNxx3g1aWpISotUHUgPp6mlM9BYi5+n6jh6Ng1xs6pDIIQQRo1GIILKIIEIvc2NGzd+++234uJiGxubPXv2dHPvt27dOnnyJCFk2bJl48ePJ4Tk5eWtWbOGEHL27FltbbwKBgAAAICuJUx/RKorVR1FD6ZlOYijpdH8LEfVgfRw/XS0HRwlhQWqjqNnY7QMCVH9KESJGtVAVIsw+iYkEKFXSUpKOnDgAP1cU1PT/QEIhcKKigpCSGNjY/f3DgAAAAAAAKBcWIUZCBKI0Ms8ePCAEGJmZrZhwwZra2tVhwMAAAAAAADQs0kII1aPoX/qEUUfhQQi9Cp1dXWEEE9Pz2HDhqkkgOnTp0+fPl0lXQMAAAAAAAAoHSNhJOoxBBEJRBVCAhF6IQ0NDVWHAAAAAAAAANAbSIja1EDEJGbVQQIReolff/01IiKCfo6KioqKijI3N//5559pS11d3ZUrVxISEsrLy+vq6oyNjS0tLadMmeLj48PlctmTlJeX//Wvf9XX1w8PD8/NzT1z5syTJ08aGxutrKzeeeedqVOncjicJ0+eREZGZmVliUQiHo/n7+/v7+/PpiyLioqCg4NpPAMHDpSPMz4+ftu2bRwOJywsTH6HlStXFhQUzJgxg54EAAAAAAAAQLVQAxEIEojQa+jp6ZmYmNTW1opEIh0dnf79+xsaGtJN5eXla9euFQgE7M4lJSUlJSWpqalxcXEbN27kcGSXtUpKStq2bVtzczP9taCg4NChQ7W1tcbGxvv372cXfsrLywsNDa2oqAgMDFQwTjc3N21tbZFIlJSUNHnyZOlNfD6/oKCAEOLn59f+CwAAAAAALbtx48Zvv/1WXFxsY2OzZ8+ebu791q1bJ0+eJIQsW7Zs/PjxhJC8vLw1a9YQQs6ePautrd3N8QAAtBfDMGoyAhEZRBVCAhF6iXnz5s2bN2/Xrl337t2bMmXK8uXL2U379u0TCAQ6Ojrz5893cXHR1dUtKSm5cuVKSkpKXFzcgwcPPD09pU8lFAp37dplb2//4Ycf2tjYZGVlHTp0qKqqKjw8nGGYQYMGBQUFOTo65ufnHzp0qLi4+Pz58/Pnz+/Xr58icfbr18/d3T0+Pj4hIUEmgRgbG0sIMTMzc3Z2VsYlAQAAAACSlJR04MAB+rmmpqb7AxAKhRUVFYSQxsbG7u8dAKDz1GcRFQkyiKqDBCL0ckKhMD09nRASEhLCjuyztbUdPXr06tWrnz9/npWVJZNAbGpqMjc33759O30h7OnpKRKJdu7cKRaLDQ0Nd+/e3b9/f0KIq6vrypUrN23a1NzcXFRUZGdnp2BIXl5e8fHxKSkpIpFI+p0zTSD6+vrKj4hUrk8++YQuVw3Qo82fP3/jxo2qjgIAANQdfewxMzPbsGGDtbW1qsMBAOh5JBKiJouoIH+oQkggQi8nEAgmTJhACBk7dqx0O5fLdXBweP78eW1trfxR8+fPl07tjRgxgn6YO3cuzR5Sw4cP19DQkEgk0vOjX8vDw0NDQ6OxsTE1NdXDw4M2duf85ezs7OTk5K7uBaCr6evrGxkZffTRR6oOBAAA1FpdXR0hxNPTc9iwYSoJYPr06dOnT1dJ1wAASiEhRKwemTuJqgPoy5BAhF7OwsJi3bp18u1CoTAvL6+1o4YMGSL9K5s0dHR0lG7ncrk6OjoNDQ3tCklfX9/FxSU1NTUhIYFNINLhhzwez97evl1nA+izbt++ffv27eXLl3f1oF0AAOgF2CXvAACgvSSMukxhVo8o+igkEKGvEAqFz549KyoqKioqysnJ+fPPP0UiUWs7Sw8zVKS9vby8vFJTUxMTExmGobkPmkD08/PrhlTIb7/9xq4PA9BDeXl5ZWdnqzoKAABQa7/++mtERAT9HBUVFRUVZW5u/vPPP9OWurq6K1euJCQklJeX19XVGRsbW1paTpkyxcfHh8vlsicpLy//61//qq+vHx4enpube+bMmSdPnjQ2NlpZWb3zzjtTp07lcDhPnjyJjIzMysoSiUQ8Hs/f39/f359NWRYVFQUHB9N4Bg4cKB9nfHz8tm3bOBxOWFiY/A4rV64sKCiYMWMGPQkAQPdTn0VUGMxhVh0kEKH3Ky4uPnHiRGJiYlNTE9toaWnJMExJSYlKQvLy8jp8+HB1dXVWVpazs3M3r7+sr6/fDb0AdCnpf9oBAAC0SE9Pz8TEpLa2ViQS6ejo9O/f39DQkG4qLy9fu3atdBWakpKSkpKS1NTUuLi4jRs3yr/TTUpK2rZtG/sWtqCg4NChQ7W1tcbGxvv372f+80/rvLy80NDQioqKwMBABeN0c3PT1tYWiURJSUkyi+x181Nie/HrGp5UCQYP0BtuZKBWh/QsBZVV6YUltsaGLjxLtTqkZ8kvKUvLf25rbuZqb6NWh/QOEoaI1aMGonqkMfsojOSHXq64uPizzz6LjY3V0tKaOnXq3/72t507d4aHh//0009sZcPuZ2Ji4uTkRAhJSEgg/xl+6ODgwOPxVBUSAAAAQC8zb968Y8eO0ULYU6ZMOXbs2O7du+mmffv2CQQCHR2dxYsX79y58+DBg5s3b3Z3dyeExMXFyS83JxQKd+3aZW9v/89//jMsLGzz5s1GRkaEkPDw8AMHDgwaNOirr74KCwv79ttvLS0tCSHnz59XfM3lfv360a7pk6E0+pRoZmbm7Ozc4evQde4WlXx0N+HXnGfqdkjPEpOZu+jY6V/i2rHIYfcc0rNcf5S+4Lv9R67FqNshvQPDEIl6/CCBqEIYgQi9XGRkZE1NDY/H27lzp8zIO9VO46UTMOPj45csWcLOX1ZhPAAAAAB9hFAoTE9PJ4SEhISwD2C2trajR49evXr18+fPs7KyPD09pQ9pamoyNzffvn07XWfP09NTJBLt3LlTLBYbGhru3r2bFrpxdXVduXLlpk2bmpubi4qK7OzsFAzJy8srPj4+JSVFJBJJL+VHnxJ9fX1R8BcAVEhC1KYGIqYwqw4SiNDL0Sppfn5+MtlDhmGysrJUFBQhhHh7e588ebKwsDAhIYHOTPH19VVhPAAAAAB9hEAgmDBhAiGEDk5kcblcBweH58+f19bWyh81f/586dQeO5dl7ty50mWyhw8frqGhIZFIpOdHv5aHh4eGhkZjY2Nqaiq7yF7b85efPXv24sULmUYdHR3FOwUAUJAaLaKi6gD6MiQQoZfT1dUlhJSVlcm0nzlzhhZAFIvFKgiLEB6PZ21tzefzQ0NDCSFvvPGGmZmZSiIBAAAA6FMsLCzWrVsn3y4UCvPy8lo7asiQIdK/sklDR0dH6XYul6ujo9PQ0NCukPT19V1cXFJTUxMSEtgEIh1+yOPx7O3t5Q/R0dExMJCtCYiBigDQFSQMUZdFVNQjjL4JCUTo5YYNG5aZmRkTE2NkZOTu7s7lcgsLC69fv56RkcHhcBiGSU5OTklJcXZ21tPT6+bYvLy8+Hx+ZWUlwfxlAAAAgG4nFAqfPXtWVFRUVFSUk5Pz559/ikSi1naWHmaoSHt7eXl5paamJiYmMgxD84BslZsW04JWVlZWVlYyjZWVlXTQIgCAEkkYBouoABKI0MstWLAgNja2vLw8MjIyMjKSbff09PTz89u9e3dlZeXWrVu//fZbV1fXbo7N29ubhsThcHx8fLq5dwAAAIA+q7i4+MSJE4mJiU1NTWyjpaUlwzB0kkr38/LyOnz4cHV1dVZWlrOzs3quv2wX/pt846mc/FM5+dItV/4yiV0xuXsO6VkM1n0l33jsj6RjfyRJt9z/9BN2xeTuOaRn0Zn3/+Qbf46+9XP0LemWhO//wa6Y3D2H9EpihjSpR+ZONfMHgRCCBCL0evr6+vv27YuIiHj48GFJSYmOjo6Tk9PUqVNpyZvnz59funTJxsbGxMSk+2MbMmSIqalpeXm5q6uroaFh9wcAAAAA0AcVFxd/9tlnNTU1enp6kyZNGjp0qK2tLY/Ho8+NqkogmpiYODk5ZWdnJyQkODs70+GHDg4OPB5PJfEAALDEhGlSj7F/ElUH0JchgQi9yvr169evXy/TOHDgwKCgoBb3DwwMDAwMZH81NTW9ePGi/G7a2totthNCzpw5I/2rlZWVzJ4ODg6tHdvU1PTq1SuiZi+WAQAAAHq3yMjImpoaHo+3c+dOmXX2mpubVRUVIcTLyys7Ozs+Pn7JkiXs/GUVxiMv94PZ0r+eynn25YO09x3tvvX8r6k8XKk5191zSM9Ssfu/xgYei0v67PzlJV6j97z7jnS79BfsnkN6lrrIY9K//hx9a82Rk3+dMvHAR0uk27kaGt18SK/UzBB1SSB2TRilpaVff/11VFRUSUkJj8ebN2/eli1b5OvMtiguLu677757/PhxcXHxsGHDxo8f/9VXXxkbGyuxCzWBBCKAyiQnJ9fX12tqanp7e6s6FgAAAIC+Ijs7mxDi5+cnkz1kGCYrK0tFQRFCiLe398mTJwsLCxMSEuj8ZV9fXxXGI0/zv5NNNPfE4ci2d/8hPYvmfyebuBocQogGh6PZehKqew7pWTS5XOlfaQpPg8ORae/+Q3olCUOa1WPsX1fkDwsLC728vOi69gMGDHj27Nn3339/+fLluLg4IyOjto89cODA2rVrJRIJl8s1NjZOSUlJSUk5ffr0vXv3hg0bppQu1Ecv+bMDoMdhGObSpUuEkNGjRyur9jYAAAAAvJauri4hpKysTKb9zJkzdP6yWKyaKls8Hs/a2poQEhoaSgh54403zMzMVBIJAIC0ZoZpUo+frkhjrly58sWLF6NGjXr8+HFNTU1ycrKdnV1mZuYXX3zR9oGFhYUbN25kGGb37t319fWlpaXPnj3z8fEpKyv7+OOPpReM7nAXagUJRIDulpGRERsbGxoamp6eTgiZMWOGqiMCAAAA6EPoqJCYmJiwsLDHjx9nZmbGxMR8/vnnp0+fpusdJycnp6SkNDQ0dH9sXl5ehJDKykqifvOXAaDPEqs6b8j+iJU9BLGsrCwqKkpXV/f8+fPDhw/ncDju7u7nz58nhJw5c0YoFLZx7KFDh4RCYUBAwKefftqvXz9CiJ2dXXh4uKam5u3bt/l8fue7UCuYwgzQ3WJjY6OiouhnDw+P7l/9GaA3cXR0VMp5NDQ0cnJylHIqAABQcwsWLIiNjS0vL4+MjIyMjGTbPT09/fz8du/eXVlZuXXr1m+//bb7n9O8vb1pSBwOx8fHp5t7BwBokZiQZrUogUiUHsWZM2eam5unTp1qb2/PNrq5ubm5uT18+PDy5csBAQGtHfv48WNCiMwOtra2w4YNe/z48ZMnT2xsbDrZhVpBAhGgu1lbWxsZGYnFYl9f3yVLlnB6SzEXgO40YsSIpqam3NzcvLw8pZyQw+H89NNP48ePHz58uFJOCAAAaouuthwREfHw4cOSkhIdHR0nJ6epU6eOHTuWEPL8+fNLly7Z2NiYmJh0f2xDhgwxNTUtLy93dXU1NDTs/gAAAOSJ1WcRFWWf8NatW4QQf39/mXZ/f/+HDx/GxMS0kd0rLS01MDCQTgtSenp6hJC6urrOd6FWkEAE6G7Tpk2bNm2aqqMA6NkeP36cm5urxBMyDBMcHHzo0CEkEAEAepn169evX79epnHgwIFBQUEt7h8YGBgYGMj+ampqevHiRfndtLW1W2wnhJw5c0b6VysrK5k9HRwcWju2qanp1atXpIfMX1401H7RUNl/OavDIT3LX8d5/HWchxoe0rMs95+83H+yGh7SO4gZpkmiHglEZUdBS98OHTpUpp3Oc6JbW/PHH3/IN1ZWVmZmZhJCXFxcOt+FWkENRAAA6MEkEgnTafv371f19wAAACCEkOTk5Pr6ek1NTW9vb1XHAgDwv5oZ0qQeP0pPIJaWlhJC5Ed808WR6VZFNDY2vnz58vfff585c2Ztbe3ChQudnJyU24XKYQQiAAAAAACA6jEMc+nSJULI6NGj+/fvr+pwAAD+l/8b5nNcB7W46crj4l+T+Mrt7p/vDLc3afnPQB1NJQ+Do/k7msuT1t7s3tixY1NTU+nn4ODgvXv3Kr0LlUMCEQAAAAAAQJUyMjKqqqpSU1PT09MJITNmzFB1RAAA/ye7rD6nrK7FTTll9Uqf2xyTXWaoW93ipllvWim3L6bN2o4ikUjB87i4uDAM8+zZs9ra2n/9619ubm7BwcHK7ULlkEAEAAAAAABQpdjY2KioKPrZw8Oj+1d/BgBoQ05Z3bnUl93WXUx2WWubJjiaduyc8+bNq6mpYX/19fX96quvCCEWFha1tbXV1bL5yqqqKkKIpaWlgucPCwsjhEgkklOnTi1btuzjjz+2tLScPXu2ErtQOSQQAQAAAAAAVMna2trIyEgsFvv6+i5ZsoTD4ag6IgCAXuXu3bsVFRXsr2xFQgsLi9zcXPnsHm1pb3ZPQ0MjMDAwIyNj+/bthw8fZhOISuxChZBABOW4devWyZMnCSHLli0bP368qsPpDnl5eWvWrCGEnD17Vltbm/TJiwAAAAAAnTdt2rRp06Yp62wD/d/pT9RivdQeqt/QYRpcDpdno+pAejaOpibH1EJzhLuqA+nZOE+fE7FY1VH0BuXl5S22W1hYEEKePn0q056fn89ubVFRUdGJEyeMjIzYqcosHx8f9gyd6ULdIIEIyiEUCmk6v7GxUdWxqEyLF2HLli2pqamLFi1auHCh6kIDAAAAgL6i9Ohx7dISVUfRg5ks+VDf0qg5P1fVgfRw2jqEkMY/bqs6jp6NGexMNLiqjqI3mzhx4vnz52/cuPG3v/1Nuv369euEkAkTJrR2oK6u7hdffKGtrb1s2TJNzf/KrVVWVhJCeDxeJ7tQN0ggAgAAAAAAAABAyyYNNdPXUYv0kbOFvnJP+N57761bty4mJqa0tNTc3Jw25uXlJSYmGhgYzJkzp7UDDQ0NhwwZ8vTp03//+98BAQFsO8MwkZGRhJBRo0Z1sgt1oxb/BUAvMH369OnTp6s6ChXDRQAAAAAAAIBe5qNxdh8RO1VH0SXMzMxmzZp1/vz5RYsWnT171sDAoKysLCAgQCKRBAYG6urqsntu27ZNJBJNnjzZz8+PtqxcuXLNmjUrVqzQ0tL6y1/+oqmpWV5e/s0330RFRenr64eEhLS3CzWHBCIAAAAAAAAAAPRFBw4cSEhIuHHjhpmZmZOTU0ZGBsMwI0aM+Oc//ym927Zt2+rr6zU1NdkEYkhIyMWLF2/evDl79mxNTU1DQ0NaaVFPT+/nn3+2tbVtbxdqDglEUI6ioiJaOvTXX38dOHAg26Kvrx8eHl5VVRUREfHgwYOqqiozM7Phw4d/8MEHpqYtrL+ekpJy9erVzMzMuro6U1PToUOHzpw509nZWX7P2traCxcuxMfHl5aW6urqDhs2bM6cOW+++ebGjRufPHmyZ88eR0dHQkhqauqWLVt0dHQiIiJkznD79u09e/ZYWlr+9NNP0u11dXVXrlxJSEgoLy+vq6szNja2tLScMmWKj48Pl9tW+QmZi7Bv376bN2/STeHh4eHh4U5OTu++++62bds4HE5YWBi9UNJWrlxZUFAwY8YM+TqsAAAAAAAAAKBcPB4vJSXlq6++ioqKys7OtrW1nT9//pYtW/T1XzNdmsvlXr9+/ejRo2FhYbm5uQKBwM3NbcyYMZs3b7ax+a9VmDrchVpBAhG6XFFR0aZNm9g1j16+fPny5cu4uLj9+/dLrzckkUhCQ0OvXbvGthQXFxcXF9+7d2/+/PmBgYEcDofdVFBQsGXLFnYddJFIlJiYmJiY+OGHH3Yy2vLy8rVr1woEAralpKSkpKQkNTU1Li5u48aN0mG0bcCAASYmJgKBoLm5WVdXV09Pz8DAwM3NTVtbWyQSJSUlTZ48WXp/Pp9fUFBACGFfaAAAAAAAAABAlzI3Nw8NDQ0NDW1jn7q6OvlGDQ2NoKCgoKAgpXSh5pBAhK4lFot37dr16tWrlStXurq6SiSSW7dunTlzpqGh4fjx4xs3bmT3PH36NM0eent7T5w40dzcnM/nX7x4MTc3NzIycuDAgbNnz2bPuW3bNpo9nD59uru7u5aW1qNHjy5evHj8+HEdHZ3OBLxv3z6BQKCjozN//nwXFxddXd2SkpIrV66kpKTExcU9ePDA09NTwVPRP0foKszz5s1jV2F2d3ePj49PSEiQSSDGxsYSQszMzFoccQkAAAAAAAAAoBJIIELXamhoKCgo2L9//+DBg2nL+++/LxAIrly5kpmZye5WXl5OFypatGgRm2gbMmSIr6/vzp07//jjj1OnTk2ePJmO771582ZRUREhZMOGDePHj6c7u7u7jxw58ttvvxUKhR2OVigUpqenE0JCQkLYYYC2trajR49evXr18+fPs7KyFE8gtsbLyys+Pj4lJUUkEmlra7PtNIHo6+ur+CDHjpk9e/a9e/e6tAuArlZTU6PqEAAAgLx48WLFihX0cx+swZKVlbV+/Xr6ecGCBYGBgaTPXxMAAOitkECELufv789mD6kxY8ZcuXKlqqqKbbl9+7ZEIhk8ePD8+fOl9+RyuStWrHjw4MGrV6/S09PHjRtHCDl//jwhxMPDg80esqf19PRMSEjocKgCgWDChAmEkLFjx8qE4eDg8Pz589ra2g6fnOXh4aGhodHY2Jiamurh4UEbu3P+cl1dnfTFB+ih3N3dp0yZouooAAAAFBIdHV1RUUEI0dTUlHniBQAAUH9IIEKXY3NkLPlCoTR35ubmpqGhIbPJwMDA3t4+JycnOzt73LhxIpGosLCQEDJt2jT5vqZMmdKZBKKFhcW6devk24VCYV5eXodPK0NfX9/FxSU1NTUhIYG9OHT4IY/Hs7e3V1ZHrRkwYICRkVFX9wLQ1SZPnrxjxw5VRwEAAKCQ6Ojo7OxsQggtlaPqcAAAANoHCUTocpaWlq/dh05JjoqKioqKam0fWrK0pKSE/srj8eT3abGxA4RC4bNnz4qKioqKinJycv7880+RSKSUM1NeXl6pqamJiYkMw9AJyzSB6Ofn19Xzlwkh//73v7u6CwAAACl9LNEAACAASURBVOgLNDU12YUm8XqSwjUBAIBeCQlE6HL9+/d/7T5lZWWEEA0NDS6X29o+NIVH9ySEGBsby+/TYmO7FBcXnzhxIjExsampiW20tLRkGIbNXXael5fX4cOHq6urs7KynJ2dsf4yAAAA9ESWlpY//PCDqqNQL7gmAADQKyGBCGrByMioqqoqMDDw3XffbXvPgQMH0g+VlZWDBg2S2SoQCBTvtL6+XqaluLj4s88+q6mp0dPTmzRp0tChQ21tbXk8nr6+/r59+5SYQDQxMXFycsrOzk5ISHB2dqbDDx0cHJQ1ghIAAACgG7S4ikh8fPy2bdtoY3Bw8OTJkyMiIuLi4srLy42MjOzs7N577z1HR0eZUwmFwsuXL//xxx+FhYWNjY3GxsZvvvnmX/7yFycnJ/l+GxsbL168ePfu3aKiIl1dXRcXlw8++KC+vl4+mAsXLhw7dow2bt261d3dnT3JgwcPvv32WzbOGTNmsJtKSkrOnz//9OnTkpKSV69emZiYmJmZTZw4ccKECVpaWu29Jvv27bt586b0N501axYh5OzZs0eOHLl27Rptl15LkJI+8ODBg7a2tm13DQAA0HWQQAS1YGVllZeX9+LFi9fuyU6IfvnypXwC8eXLl4p3WlxcLNMSGRlZU1PD4/F27twpU6ixublZ8TMrwsvLKzs7Oz4+fsmSJez8ZeV2AQAAAKBatbW1n376KfuMV1paWlpampSUtGbNmokTJ7K7PX/+fOvWreXl5WxLaWnpzZs3b968uWDBgkWLFknXeKmsrNy8eTN7TpFIdP/+/eTk5KVLlyol5uvXr//P//yPWCxmW2hZm7S0tHPnzu3evVuR6TUK8vPzYxOIDx48kE4gMgzz8OFD+nnw4MHstGi18kLYmNXQwOvXz7m/nlod0rMUVFSlvyy2MTYcybNSq0N6loKKyvQXRTYmRiMHy/4jUbWHAPQasgtWAKgEHXn36NEjoVAos6m+vn7NmjUhISG5ubmEkAEDBtDU3tWrV+XPExMT0+L5GxsbZYoYSiSSlJQUmd1oZWs/Pz+Z7CHDMFlZWe36Rq/l7e1NCCksLExISKDzl319fZXbBQAAAIBqnT17Vv4NsUQi+eWXX9i5ILW1tV9++aV09lBT8/9GOURERFy8eJH9lWGYXbt2yZ/z1atXR48e7XzAeXl5oaGh0tlDaYWFhaGhoe09p4eHR0BAAFsPkcvlBgQEBAQEcLncESNGsO05OTnSk2meP39eVVVFP48fP74bymR3QGy1YGVm9pnidkzT6Z5DepYbmTkf/Bx+NDZR3Q7pWW5k5HzwU9jRe+1YUbN7DgHoNTACEdSCn59fREREZWXl6dOnP/zwQ/YJiWGY8PDwvLw8IyMjBwcH2jhu3Lhr164lJibGxcWNGzeOPUlqaur9+/dlzmxoaEj+8wp37NixbHtMTAyfz5fZWVdXl0iVWWSdOXOGzl9u7WnytSQSiUwLj8eztrbm8/n0MfSNN94wMzPr2MkBQFnWr1+/efNmVUfRJdpV4QEAQFlEIpGVldWsWbPMzc1zc3PPnTtH3+kKBIK0tDT6PpU+BNL933nnncDAwH79+qWlpe3du7e6upoQEhYWNnHiRAMDA0JIcnLy48eP6c5cLve9994bPnz4y5cv//Wvf0mnIDvs+vXr7LyTBQsWTJw4UVNTMysr65dffqHBpKSksIvgKcjHx8fHxyc9PZ0mBLW0tJYsWcJuHT9+PF1FkGGYlJSUSZMm0XbpV93jx4+XP21TU5N0zW5Kuev+AQAAsJBABLVgY2MzderU6OjoCxcu8Pn8KVOmWFlZVVVVRUdH0+m9ixcv1tD43wGz8+fPv3PnjlAo3LFjx4wZM9zc3LS1tdPS0i5cuKClpSXz2DR48GA9Pb2GhoYffvhBKBSOHDlSIBDExsZGRETY2dnl5+dL7zxs2LDMzMyYmBgjIyN3d3cul1tYWHj9+vWMjAwOh8MwTHJyckpKirOzs56eohMo6PPln3/+WVFRoaWlxdZwJIR4eXnx+Xz6xIz5ywAqpK+vP2HChDt37jQ0NDQ0NKg6nK4yadKkmTNnqjoKAOhbTE1N9+7dSx+cPDw8NDU1w8LC6KbCwkJCCMMwN27coC0ODg7Lly+nz05ubm7BwcHfffcdIUQkEt28eXPu3LmEkMjISPbkGzZsoClIFxeXMWPGfPLJJ/JzWdorIyODDYZWUSSEWFpaZmRk/P7774SQurq6qqqqzi/cx/L19aUJREJIUlISm0Bk5y9bW1u3OH85Nzf36dOnMo3Sj5oAAABKhAQiqIuPPvqouro6MTExKSkpKSmJbedwOAsWLHj77bfZFnNz8w0bNuzYsUMkEl2+fPny5cu0ncvlrl69eteuXdKn5XK5H3/88Z49ewQCwffff8+2Ozg4hISErFu3TnrnBQsWxMbGlpeXR0ZGSj+eenp6+vn57d69u7KycuvWrd9++62rq6uC38vGxubRo0fp6elLly51cnLavXs3u8nb25v2wuFwfHx8FDwhAChdbW3tnTt3CCGmpqa9+J9e06dPX7t2raqjAIC+xc/PT/q165tvvsl+psPxqqqq2LnMXl5e0iP7PDw8uFwunf9BS9kwDPPs2TO61dbW1svLi93ZxMRkypQpbCauw2jKkhDCvrpmGOb58+ePHj1i91Fuaexhw4aZmZnRGTAPHz4Ui8VcLrexsZEdaNni8ENCiI2Njfz8lYaGhrS0NCWGBwAAQCGBCOpCW1t706ZN9+/fj4mJycnJEQqFlpaWdnZ2AQEB7ORl1pgxY3744YezZ8+mpKRUVVX179//jTfemD9//pAhQ+TPPHHiRDMzs4iIiPz8/8/enYc1de0L419JAAMWQhgkAoIioMJBFFB/QAGxKvpwRNQqju3rIx4qpUV7tepRWu3xrQO+Cg5Xe7wVHBALokesVBBBxeAAIsSWSVQgQAgZIIAxDEl+f6x79t03CZggyNDv5+kf2WuvvfZ3r017cr5ZQ/WbN28sLS39/f2XLVsmEolUauLdllNSUp49e8bn8+l0urOz8/z58/Hc59ra2l9//dXOzs7c3Fz75woLCxMIBCUlJQqFQuVL3sSJEy0sLIRCobu7O55qDQAYXHv37o2MjBzsKAAAYOSwsLAgH6rP4eDxeMTnS5cuXbp0SWM7eDGZ5uZmYoyhs7OzyjxiJyen9w+YTqfjGz19+rS8vLy6urqmpqajo+P9W+4JhULx8/O7evUqQqi9vb2iosLFxeWPP/4gpif39DPz6NGj1bdzISaDAwAAAP0LEoigf4wdO5a8vrXGEsKkSZM0nsLfn7TcS4TFYkVFRakU9rRGoaur6969e98ZM0LIxMQkPDxcYyNr164lZrIghBwcHLR5ZGNj4507d2pssKur6+3btwjmLwMAAABghHrnWoE4M/hOOM9IXuWQ2HuEoNNPvD2RyWTnzp3LyMhQKpXkcmIs5EDw9/fHCUSEUGFhoYuLCzF/2c7Obkjtv+ySr2HviBR+Uwq/iVxy1d2N2DH5w1wyvJhEa1hw+Sy74Cy7gFzy4NsviR2TP8wlw4vJlzvUC88+eHz2wf/6+3mw82tix+QPcwkAIxUkEAEYNE+fPn3z5o2enh5evgcAAAAgo9FoeHw6eUdaAEYY8sIRnp6e48aN01gNJyLJKw8SOxQT8CYn2lNJEWI///xzZmYm/kyn02fOnDllypS//OUvd+7c+de//qVT+9qbMGGCjY0NXhSyoKDgs88+IxKIPc1fBgAAAD4w+D4KwOBQKpW//vorQsjT01N9+gkAAAAgl8txQqR/F1wDYEixtv6fMTuurq6ffvppL5XNzc319fXx3N7KykqV3ZBfvHjR+73wzA8CXnZQpQKRPXR1df3+++/xjGaNlfsRhUL5+OOPf/nlF4RQTU1NRUVFbW0tPjXUlsl+7j2TfPgLv2nfq+rlVmO+cxhPLqeS3suHuWR4ER/9gXx4ll2w9cqN/+PtdWRFCLmcRnrAD3PJ8CI+/iP58Gze460p1/+P78wjK0PJ5f+rTz7IJQCMVJBABOBDKy0tbW5uLikpef78OUIoODh4sCMCAAAAABgcVlZWxOzgkpIScgJRqVQ+fPgQnzIxMXF3d6dQKPb29nhDlZqamocPH/r4+ODKQqHw1q1b6u0zGAzi8x9//EHk4+Ry+e3bt1UqNzQ0EJ8nTZpEZA9bW1uLiore+1kRcWuV1CdCyM/PDycQEUL/9V//hT/Y29v3NCRzsKhkSWiIghCi9Jo9+TCXDC96/96iB6NRKQghKpWiUv7hLxle1B6QihCiUnTqkwG5BICRChKIAHxobDab2CJwxowZ2m/oDAAAAAAwwtBoNF9f3/v37yOESkpKLl++HBoaOmrUKD6ff+vWLWJlwIiICPyVadmyZcRGybGxsStWrHBxcWloaPjll1+I/VXIbGxsiM8ZGRkGBgaurq7Nzc0ZGRmvXr1SqUyeFHLv3r2JEyc6ODg0NjZeuHBBZfRiHxDbOnd1dYlEIpXtZezs7Ozt7WtqahBCFRUVuBDmLwMAABg6IIEIwIc2btw4JpMpl8v9/Pw+//zzdy4uDgAAAAAwgq1du7agoABn6PBGzMQ8ZWzcuHHz5s3Dn318fMaPH19dXY0QksvlycnJRDUqlapQKFQad3BwsLa2xkMLFQrF1atXiaSkOisrKwaDIZFIEEIikSg2NlZjNfW7aMPS0rK8vBx/3rVrl6Wl5ffff6+vr09U8Pf3v3DhAvmSoTZ/GQAAwJ8ZDLsFIwqNRktPT09PT3d0dBzsWHq0YMGCc+fOXbx4MSIigpgaAwAAAADw58RisXbv3k0e/UfOHk6ePPnAgQMGBgb4kEKh7NixQ2X4HkKISqWuXr1avXF9ff0vvviCnKfDjIyMVq1apVJIoVAiIyPVG/Hy8goN/Z/1zvA4QV0Rs60RQjwej8PhqOzi4ufnRz4cP368ra1tH24EAAAADARIIAIAAAAAAAAGk5ub2+nTp5cvXz5hwgQ6nW5gYGBjY+Pt7f3dd98dPHjQ2NiYXNna2vr48eOffvqpjY2Nvr7+6NGjPTw8Dhw40NOyMNOmTYuPj/fx8WGxWHp6ehYWFkFBQSdOnND4e7O3t/f+/funTZvGYDBMTExmzJgRFRUVExMzffp0os6ZM2f68Iw+Pj6RkZHjxo0jkqEqWCyWk5MTcQjDDwEAAAwpFJUfvgAAQ1NnZ2dWVpaHhwd5s0IAQL84duxYdHQ0QujkyZMax54AMCgSExPXr1+PENq/f/+OHTv63I5cLv/tt98QQlOnTrWzs+u3+AAYYioqKrZt24Y/r1ixYu3atYMbTx+kpKRcvHgRfz516hR5AUcticXi/Pz8cT+fMWji93d0fyLmn/8fYxazu7pqsAMZ5gzoo2Z93PHw7mDHMbzl207uotKcnZ2dnZ0HOxbwZwcjEAEAAAAAAABg8DU3N+MPEyZM6EP2EAAAABg4kEAEAAAAAAAAgEH2xx9/3L59G39WWQ8RAAAAGHSwC/OA4/F4ERERCKGLFy+amJj0Xjk3N/f8+fMIoQ0bNnz88ccIoVevXm3evBkhdOXKlZ4WTCHI5fIlS5YghI4cOTKUdxERCATJyckVFRUymWzixIkrVqwYxGh1ekEDR6cXDQAAAAAARozDhw+Xl5c3NTXhw1GjRs2fP39wQwIAAABUQAJxaJHJZCKRCCHU0dEx2LEMlNLS0j179shkMnwoEAiePHmydetWnDAFAAAAAADgT0UqlRLZQ4RQWFjYIP6qDQAAAGgECUTwQXV0dBw9elQmk82dO3ft2rUUCuXSpUuZmZmnT5/28PAwMjIa7AABAAAAAAD4oIyMjGg0moGBgbW19YIFC2D4IQAAgCEIEohDy8KFCxcuXDjYUQygyspKPp/PYDAiIyP19PQQQl988UVJSUljY2NlZeW0adMGO0AAAAAAADAsTZo0KT09fbCj6IutW7du3bq1Hxuc8J+nTD4a3Y8N/unoG3TVvqbZTxzsOIY5KtXAaYqB05TBjmN4o7Afoq6uwY4CAIQggQg+sPr6eoSQlZUVzh4ihGg02rhx4xobGxsaGiCBCAAAAAAAwPuSiFBH+2AHMZwxzDufF3W/qhzsOIY5Ot3A3gG1iAY7jmFOqRjsCAD4b5BAfF9dXV0ZGRn379+vr6+nUqk2NjZeXl6hoaGjRo1SrywUCpOTk4uKilpbWy0sLKZMmbJ69eoxY8YQFbTf0EMsFqelpRUUFAiFwtGjR7u4uCxbtmziRNVfyXCD5ubmCQkJPB7vzJkzHA4nKCho48aNRJ2ioqJbt26Vl5e3t7dbWFg4OTktWrRo8uTJ6u0YGxsnJSU1NzenpKQUFBQ0NzdbWlq6uLisXr3awsJCm+6yt7dHCFVXV3d1denr6+MOfPXqFUKIyWRq0wKhtLT0+vXrtbW1AoGAwWBYW1sHBwfPmjWLQqGQq/XvC8LkcnlmZua9e/e4XK5MJmMymW5ubiEhIQ4ODupt6lQZAAAAAAAAAAAAYKiBBOJ7aWlp+e6776qrq4mSioqKioqKrKys2NhYMzMzcuXq6uqDBw+2tbXhQx6Px+Px8vPzjx07xmKxdLpvWVnZ3r17pVIpPpRIJA8fPnz8+PFXX33V0yVNTU07d+4Ui8XkQoVCcerUqczMTKKksbGxsbExLy9v+fLleI1ClXZ4PN6uXbuEQiE+bGhoaGhoyM/Pj4+Pt7Kyemfkjo6OJiYmra2tFy9eXL9+fWdnZ3x8vEgkMjY21mn4YWJi4tWrV4lDgUAgEAhKSkoWLFgQGRlJlA/EC2ptbd2zZ09VVRX57jk5Obm5uevXrw8NDSW3qVNlAAAAAAAAAAAAgCEIEoh9p1Ao9u/fX11dTaFQlixZ4uHhYWBg8OzZs7S0NIFAEBcXt3fvXnIC7uDBgwih6OhoNzc3hUKRlZV15coVmUx27ty57du3a39fmUx24MABnD0MDg6ePn26np5ecXHx9evXT5w40VOo8fHxYrHY2dnZwcFhxowZuDw5ORlnD729vWfPnj1mzBgul5uenl5VVZWammpiYrJ48WJyO3K5PDY29u3bt1999ZW7u7tCocjNzb18+bJUKk1MTNTmKfT19cPDw48cOXLt2jV7e/uMjIzKykoajRYdHW1oaKhlDxQWFuLsoZeXV3BwsIWFhUgkSk9Px0MpfX193d3d0cC8IKVSGRsbixOCAQEBvr6+5ubmVVVVaWlpTU1NZ8+etbW19fLy6kNlAAAAAAAAAAAAgKEJEoh9x2azy8rKEEI7duzw9vbGhZMnT7azszt48GBxcXFdXd24ceOI+h0dHceOHbO2tsaHn332WUtLS3Z2dmWlbotr3Lx5s7m5Gd/Xx8cHF3p4eLi5ue3bt0/jJc3NzS0tLZs3bw4MDCRSZkKhMDU1FSG0Zs2asLAwXDhx4kQ/P79Dhw49fPjw0qVLc+bMMTY2JtqRSqU1NTXx8fG2tra4ZNWqVRKJJCMjo7y8XMv4AwIC7t279/Tp07i4OISQmZnZli1bcMpPS4WFhTjU3bt3U6lUhJC9vb2bm1tERIRQKORwOLi1gXhBRUVFJSUluNNWrFiBO9PJycnHx2fnzp11dXUJCQmenp64XKfKH1JbW1t3d/cHvikAQxkxoBsAAAAAAAAAgDpIIPbdnTt3EEKurq5Ecgrz8fEZM2ZMU1NTaWkpOT8VFBREJKcwT0/P7OzslpYWne6Ld5ebOXMmkT3EZsyYMWvWrEePHmm8KiAgYM6cOeSSu3fvKhQKW1vb5cuXk8tpNFpkZGRBQcHbt2+fP3+ucpegoCAie4h5eXllZGTgnKY2RCJRZ2cn/kyn048eParr6odNTU0IIVtbW5w9xPT19aOiooRCITHdeCBeUF5eHkLIwsJi2bJl5MQfg8FYuXLl4cOHuVxudXX1hAkTdK38IYWGhubk5HzgmwIAAAAAAAAAAGCYor67CuhBbW0tQmj27Nkq5RQKZd++fXFxccRMYUzlECHU+zYpGkmlUpyqW7BggfrZefPm9XShepw1NTUIoenTp5PTcBiDwcBZLfXRkepPQR6i+E6PHj2Kiop6/vy5lZUVlUqVyWRJSUnEWQ6HU1BQ0Nra2nsjNjY2CKH8/PzMzEyZTEaUe3h4zJ8/f+rUqfhwIF5QXV0dQsjf35/YRZrg6+trYGCAEGpoaOhDZQDA4KJQKD/99JP6fzEAAGAg8Hi8kJCQkJCQd37tQQjhpZPXr1//4MEDXPLq1St8OfGjbC/kcjmuTF6UeQgSCATHjh378ssvN2zY8OOPPw5utDq9oIGj04sGAAAABhSMQOyjrq4ukUiEENK4/4nGQm32GHknHo+HP6iMleulEFPfJRk3dePGjRs3bvR0VXt7u0qJrvu9kLHZ7NjYWIVCMXv27MjIyMzMzJ9//jkrK8vJySkoKEipVO7bt08mk506dar31OqSJUtycnLa2tpOnjyZkJAwc+ZMV1dXd3d3cmwD9IL4fH5Pl9NoNEtLy/r6elxH18ofkrOzs0Qi+fD3BWAoo1Kpf/vb3wY7CgAA0EAmk+FvNR0dHYMdy0ApLS3ds2cP8cOwQCB48uTJ1q1bP/7448ENDAAAAAAYJBD7SCQSKZVKhJCpqamWl4wePfr970vkfVR2EO6lEFNPyQkEAoQQlUql0Wg9XaX+a2efn+LNmzfHjx9XKBRBQUGRkZEUCiUkJKSiouLBgwc//fTThAkTTExMZDIZjUZ7Z47SzMzsxIkTiYmJbDZbKpXevXv37t27CCFHR8elS5f6+vpSKJSBeEFyuRz3f09tmpqa1tfX447VqfIHdurUqQ9/UwAAAAAAjTo6Oo4ePSqTyebOnbt27VoKhXLp0qXMzMzTp097eHgYGRkNdoAAAAAAgARiXxH5OPUxegOKGEgoFovVxxv2spyi+jxlJpPZ3Ny8du3aTz/9tH+D1Oj+/ftSqZTJZIaHh+MFASkUytdff11TU8Plcg8cOIBnELu4uPSS0CQwmcwtW7Zs2rTp2bNnHA6Hw+FwudyqqqpDhw6FhYWtWbNmIF4QjUZjMBgSiaSnfsYZQ7yko06VAQAAAAB6snDhwoULFw52FAOosrKSz+czGIzIyEi88MsXX3xRUlLS2NhYWVk5bdq0wQ4QAAAAALAGYl8ZGRkxGAz072mqKmpqah49eqTr9sraGDNmDP6gce08YoKzNsaOHYv+vU7fB4Bjmzx58qhRo4hCOp3+97//3dDQUCgU/vbbbwihJUuWaN8mnU739vaOiIg4efLkkSNH8PfLlJSUN2/eDNALwtOcNbapUCjw7i64Y3WtDAAAAADw51RfX48QsrKyIpaNptFoeKc7WC0aAAAAGCJgBGLf2draSiSSvLw8ld2NEUKnT5/+448/li9f7uzs3L83pdPpFhYWQqHw1q1bXl5eKmezs7O1bwpvRVJcXCyTyeh0OvnUmzdvdu3a1d3dvXnzZkdHx/cPGyGEvxGqL0RtY2OzefPm/fv3I4QsLS09PT17b6erqysmJkapVIaFhXl4eBDljo6OmzZtioiIUCqVPB7P0dFxIF6QtbV1ZWVlXl7e2rVrVUZKstlsPOMbd6yulQEAAAAwYnR1dWVkZNy/f7++vp5KpdrY2Hh5eYWGhpJ/RiUIhcLk5OSioqLW1lYLC4spU6asXr2a+M0YIcTj8SIiIhBCFy9e7H2daLFYnJaWVlBQIBQKR48e7eLismzZsokTJ6pUww2am5snJCTweLwzZ85wOJygoKCNGzcSdYqKim7dulVeXt7e3m5hYeHk5LRo0aLJkyert2NsbJyUlNTc3JySklJQUNDc3Gxpaeni4rJ69Wr1Nbg1sre3RwhVV1d3dXXp6+vjDnz16hXSfa5GaWnp9evXa2trBQIBg8Gwt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width="864" /></p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1"><span class="kw">ggsave</span>(</a>
<a class="sourceLine" id="cb28-2" data-line-number="2">  <span class="st">'figures/figure3.png'</span>,</a>
<a class="sourceLine" id="cb28-3" data-line-number="3">  g,</a>
<a class="sourceLine" id="cb28-4" data-line-number="4">  <span class="dt">width =</span> <span class="dv">9</span>,</a>
<a class="sourceLine" id="cb28-5" data-line-number="5">  <span class="dt">height =</span> <span class="dv">6</span>,</a>
<a class="sourceLine" id="cb28-6" data-line-number="6">  <span class="dt">dpi =</span> <span class="dv">300</span></a>
<a class="sourceLine" id="cb28-7" data-line-number="7">)</a>
<a class="sourceLine" id="cb28-8" data-line-number="8"><span class="kw">ggsave</span>(<span class="st">'figures/figure3.pdf'</span>, g,</a>
<a class="sourceLine" id="cb28-9" data-line-number="9">       <span class="dt">width =</span> <span class="dv">9</span>, <span class="dt">height =</span> <span class="dv">6</span>)</a></code></pre></div>
</div>
</div>
<div id="tables-for-topic-proportions-and-frex-terms-supporting-information-s2" class="section level2">
<h2>Tables for topic proportions and FREX terms (Supporting Information S2)</h2>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" data-line-number="1">get_props &lt;-<span class="st"> </span><span class="cf">function</span>(proportions) {</a>
<a class="sourceLine" id="cb29-2" data-line-number="2">  names_ &lt;-<span class="st"> </span><span class="kw">colnames</span>(proportions)</a>
<a class="sourceLine" id="cb29-3" data-line-number="3">  <span class="kw">print</span>(names_)</a>
<a class="sourceLine" id="cb29-4" data-line-number="4">  frequency &lt;-<span class="kw">colMeans</span>(proportions)</a>
<a class="sourceLine" id="cb29-5" data-line-number="5">  order &lt;-<span class="st"> </span><span class="kw">order</span>(frequency, <span class="dt">decreasing =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb29-6" data-line-number="6">  percentage &lt;-<span class="st"> </span>frequency[order]</a>
<a class="sourceLine" id="cb29-7" data-line-number="7">  names_ &lt;-<span class="st"> </span>names_[order]</a>
<a class="sourceLine" id="cb29-8" data-line-number="8">  topic &lt;-<span class="st"> </span><span class="kw">factor</span>(names_, <span class="dt">levels=</span>names_)</a>
<a class="sourceLine" id="cb29-9" data-line-number="9">  combined &lt;-<span class="st"> </span><span class="kw">data.frame</span>(percentage, topic)</a>
<a class="sourceLine" id="cb29-10" data-line-number="10">  combined &lt;-<span class="st"> </span><span class="kw">transform</span>(combined,</a>
<a class="sourceLine" id="cb29-11" data-line-number="11">                        <span class="dt">mid_y =</span> <span class="kw">ave</span>(combined<span class="op">$</span>percentage,</a>
<a class="sourceLine" id="cb29-12" data-line-number="12">                                              combined<span class="op">$</span>topic, </a>
<a class="sourceLine" id="cb29-13" data-line-number="13">                                              <span class="dt">FUN =</span> <span class="cf">function</span>(val) <span class="kw">cumsum</span>(val) <span class="op">-</span><span class="st"> </span>(<span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>val)))</a>
<a class="sourceLine" id="cb29-14" data-line-number="14">  combined<span class="op">$</span>label &lt;-<span class="st"> </span>topic_labels</a>
<a class="sourceLine" id="cb29-15" data-line-number="15">  </a>
<a class="sourceLine" id="cb29-16" data-line-number="16">  <span class="kw">return</span>(combined)</a>
<a class="sourceLine" id="cb29-17" data-line-number="17">}</a>
<a class="sourceLine" id="cb29-18" data-line-number="18"></a>
<a class="sourceLine" id="cb29-19" data-line-number="19">labels_<span class="dv">30</span> &lt;-<span class="st"> </span><span class="kw">labelTopics</span>(stm_<span class="dv">30</span>, <span class="dt">n=</span> <span class="dv">20</span>)<span class="op">$</span>frex <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">t</span>() <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.tibble</span>()</a>
<a class="sourceLine" id="cb29-20" data-line-number="20">labels_<span class="dv">30</span> &lt;-<span class="st"> </span>labels_<span class="dv">30</span> <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">mutate_all</span>(<span class="kw">funs</span>(<span class="kw">str_c</span>(., <span class="dt">collapse =</span> <span class="st">', '</span>))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb29-21" data-line-number="21"><span class="st">  </span><span class="kw">slice</span>(<span class="dv">1</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">gather</span>(topic, frex_terms) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb29-22" data-line-number="22"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">topic =</span> <span class="kw">str_replace</span>(topic, <span class="st">'V'</span>, <span class="st">'Topic '</span>))</a>
<a class="sourceLine" id="cb29-23" data-line-number="23">labels_<span class="dv">30</span><span class="op">$</span>label &lt;-<span class="st"> </span>topic_labels </a>
<a class="sourceLine" id="cb29-24" data-line-number="24"></a>
<a class="sourceLine" id="cb29-25" data-line-number="25"></a>
<a class="sourceLine" id="cb29-26" data-line-number="26">props_<span class="dv">30</span> &lt;-<span class="st"> </span><span class="kw">get_props</span>(props[, <span class="dv">1</span><span class="op">:</span><span class="dv">30</span> ])</a>
<a class="sourceLine" id="cb29-27" data-line-number="27"></a>
<a class="sourceLine" id="cb29-28" data-line-number="28">comb_<span class="dv">30</span> &lt;-<span class="st"> </span><span class="kw">left_join</span>(labels_<span class="dv">30</span>, props_<span class="dv">30</span>, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">'label'</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb29-29" data-line-number="29"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">proportion =</span> <span class="kw">round</span>(percentage, <span class="dv">2</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb29-30" data-line-number="30"><span class="st">  </span><span class="kw">select</span>(label, proportion, frex_terms) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb29-31" data-line-number="31"><span class="st">  </span><span class="kw">arrange</span>(<span class="kw">desc</span>(proportion))</a>
<a class="sourceLine" id="cb29-32" data-line-number="32"></a>
<a class="sourceLine" id="cb29-33" data-line-number="33"><span class="co"># create data table</span></a>
<a class="sourceLine" id="cb29-34" data-line-number="34">frex_table &lt;-<span class="st"> </span><span class="kw">stargazer</span>(comb_<span class="dv">30</span>,  <span class="dt">summary =</span> <span class="ot">FALSE</span>, <span class="dt">rownames =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb29-35" data-line-number="35"></a>
<a class="sourceLine" id="cb29-36" data-line-number="36"><span class="co"># store as tex file</span></a>
<a class="sourceLine" id="cb29-37" data-line-number="37"><span class="kw">write_file</span>(<span class="kw">str_c</span>(frex_table, <span class="dt">collapse =</span> <span class="st">'</span><span class="ch">\n</span><span class="st">'</span>),</a>
<a class="sourceLine" id="cb29-38" data-line-number="38">           <span class="dt">path =</span> <span class="st">'figures/ted_topicterms.tex'</span> )</a></code></pre></div>
</div>
<div id="regressions" class="section level2">
<h2>Regressions</h2>
<div id="youtube-sentiment-figure-4" class="section level3">
<h3>YouTube Sentiment (Figure 4)</h3>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb30-1" data-line-number="1">pal &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'#377eb8'</span>, <span class="st">'#e41a1c'</span>, <span class="st">'#4daf4a'</span>)</a>
<a class="sourceLine" id="cb30-2" data-line-number="2"></a>
<a class="sourceLine" id="cb30-3" data-line-number="3">df_reg &lt;-</a>
<a class="sourceLine" id="cb30-4" data-line-number="4"><span class="st">  </span>df <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(</a>
<a class="sourceLine" id="cb30-5" data-line-number="5">    sentiment,</a>
<a class="sourceLine" id="cb30-6" data-line-number="6">    yt_date_num,</a>
<a class="sourceLine" id="cb30-7" data-line-number="7">    yt_views,</a>
<a class="sourceLine" id="cb30-8" data-line-number="8">    factor_female,</a>
<a class="sourceLine" id="cb30-9" data-line-number="9">    factor_white,</a>
<a class="sourceLine" id="cb30-10" data-line-number="10">    <span class="kw">starts_with</span>(<span class="st">'T_'</span>),</a>
<a class="sourceLine" id="cb30-11" data-line-number="11">    <span class="op">-</span>T_<span class="dv">1</span></a>
<a class="sourceLine" id="cb30-12" data-line-number="12">  )</a>
<a class="sourceLine" id="cb30-13" data-line-number="13"></a>
<a class="sourceLine" id="cb30-14" data-line-number="14">reg2 &lt;-<span class="st"> </span><span class="kw">glm</span>(sentiment <span class="op">~</span><span class="st"> </span>yt_date_num  <span class="op">+</span><span class="st"> </span>yt_views ,  <span class="dt">data =</span> df_reg)</a>
<a class="sourceLine" id="cb30-15" data-line-number="15">reg3 &lt;-<span class="st"> </span><span class="kw">glm</span>(sentiment <span class="op">~</span><span class="st"> </span>yt_date_num <span class="op">+</span><span class="st"> </span>yt_views   <span class="op">+</span></a>
<a class="sourceLine" id="cb30-16" data-line-number="16"><span class="st">              </span>factor_female <span class="op">+</span><span class="st"> </span>factor_white,</a>
<a class="sourceLine" id="cb30-17" data-line-number="17">            <span class="dt">data =</span> df_reg)</a>
<a class="sourceLine" id="cb30-18" data-line-number="18">reg_sent &lt;-<span class="st"> </span><span class="kw">glm</span>(sentiment <span class="op">~</span><span class="st">  </span>. , <span class="dt">data =</span> df_reg)</a>
<a class="sourceLine" id="cb30-19" data-line-number="19"></a>
<a class="sourceLine" id="cb30-20" data-line-number="20"></a>
<a class="sourceLine" id="cb30-21" data-line-number="21">sent_pred &lt;-<span class="st"> </span><span class="kw">ggeffect</span>(reg_sent, <span class="dt">x.as.factor =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb30-22" data-line-number="22"></a>
<a class="sourceLine" id="cb30-23" data-line-number="23"><span class="co"># STM variables</span></a>
<a class="sourceLine" id="cb30-24" data-line-number="24">se1 &lt;-<span class="st"> </span>sent_pred<span class="op">$</span>factor_female <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb30-25" data-line-number="25"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> x, <span class="dt">x =</span> predicted, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-26" data-line-number="26"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-27" data-line-number="27"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-28" data-line-number="28"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb30-29" data-line-number="29">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb30-30" data-line-number="30">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb30-31" data-line-number="31"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb30-32" data-line-number="32">    <span class="kw">aes</span>(<span class="dt">xmin =</span> conf.low, <span class="dt">xmax =</span> conf.high),</a>
<a class="sourceLine" id="cb30-33" data-line-number="33">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb30-34" data-line-number="34">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb30-35" data-line-number="35">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb30-36" data-line-number="36">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb30-37" data-line-number="37"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-38" data-line-number="38"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Gender'</span>,  <span class="dt">x =</span> <span class="st">'Comments sentiment'</span>, <span class="dt">title =</span> <span class="st">''</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-39" data-line-number="39"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb30-40" data-line-number="40">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb30-41" data-line-number="41">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb30-42" data-line-number="42">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb30-43" data-line-number="43">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb30-44" data-line-number="44"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">5</span>))</a>
<a class="sourceLine" id="cb30-45" data-line-number="45"></a>
<a class="sourceLine" id="cb30-46" data-line-number="46"></a>
<a class="sourceLine" id="cb30-47" data-line-number="47">se2 &lt;-<span class="st"> </span>sent_pred<span class="op">$</span>factor_white <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb30-48" data-line-number="48"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> x, <span class="dt">x =</span> predicted, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-49" data-line-number="49"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-50" data-line-number="50"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-51" data-line-number="51"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb30-52" data-line-number="52">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb30-53" data-line-number="53">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb30-54" data-line-number="54"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb30-55" data-line-number="55">    <span class="kw">aes</span>(<span class="dt">xmin =</span> conf.low, <span class="dt">xmax =</span> conf.high),</a>
<a class="sourceLine" id="cb30-56" data-line-number="56">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb30-57" data-line-number="57">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb30-58" data-line-number="58">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb30-59" data-line-number="59">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb30-60" data-line-number="60"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-61" data-line-number="61"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Ethnicity'</span>,  <span class="dt">x =</span> <span class="st">'Comments sentiment'</span>, <span class="dt">title =</span> <span class="st">''</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-62" data-line-number="62"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb30-63" data-line-number="63">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb30-64" data-line-number="64">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb30-65" data-line-number="65">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb30-66" data-line-number="66">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb30-67" data-line-number="67"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">5</span>))</a>
<a class="sourceLine" id="cb30-68" data-line-number="68"></a>
<a class="sourceLine" id="cb30-69" data-line-number="69">sent_pred &lt;-<span class="st"> </span><span class="kw">ggeffect</span>(reg_sent)</a>
<a class="sourceLine" id="cb30-70" data-line-number="70">se3 &lt;-<span class="st"> </span>sent_pred<span class="op">$</span>T_<span class="dv">4</span> <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb30-71" data-line-number="71"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> x, <span class="dt">y =</span> predicted)) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-72" data-line-number="72"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-73" data-line-number="73"><span class="st">  </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">ymin =</span> conf.low, <span class="dt">ymax =</span> conf.high),</a>
<a class="sourceLine" id="cb30-74" data-line-number="74">              <span class="dt">alpha =</span> <span class="fl">0.2</span>,</a>
<a class="sourceLine" id="cb30-75" data-line-number="75">              <span class="dt">fill =</span> <span class="st">'#778899'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-76" data-line-number="76"><span class="st">  </span><span class="kw">scale_x_continuous</span>(</a>
<a class="sourceLine" id="cb30-77" data-line-number="77">    <span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">8</span>),</a>
<a class="sourceLine" id="cb30-78" data-line-number="78">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.00</span>, <span class="dv">0</span>),</a>
<a class="sourceLine" id="cb30-79" data-line-number="79">    <span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb30-80" data-line-number="80">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.6</span>)</a>
<a class="sourceLine" id="cb30-81" data-line-number="81">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-82" data-line-number="82"><span class="st">  </span><span class="kw">scale_y_continuous</span>(</a>
<a class="sourceLine" id="cb30-83" data-line-number="83">    <span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">7</span>),</a>
<a class="sourceLine" id="cb30-84" data-line-number="84">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.00</span>, <span class="dv">0</span>),</a>
<a class="sourceLine" id="cb30-85" data-line-number="85">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="op">-</span><span class="fl">0.15</span>, <span class="ot">NA</span>)</a>
<a class="sourceLine" id="cb30-86" data-line-number="86">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-87" data-line-number="87"><span class="st">  </span></a>
<a class="sourceLine" id="cb30-88" data-line-number="88"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb30-89" data-line-number="89">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb30-90" data-line-number="90">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb30-91" data-line-number="91"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Comment sentiment'</span> , <span class="dt">x  =</span> <span class="st">'Topic proportion: inequality'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-92" data-line-number="92"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-93" data-line-number="93"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-94" data-line-number="94"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb30-95" data-line-number="95">    <span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">0</span>, <span class="dt">hjust =</span> <span class="dv">1</span>),</a>
<a class="sourceLine" id="cb30-96" data-line-number="96">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb30-97" data-line-number="97">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb30-98" data-line-number="98">  )</a>
<a class="sourceLine" id="cb30-99" data-line-number="99"></a>
<a class="sourceLine" id="cb30-100" data-line-number="100"></a>
<a class="sourceLine" id="cb30-101" data-line-number="101"><span class="co"># combine plots</span></a>
<a class="sourceLine" id="cb30-102" data-line-number="102">s_c2 &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(</a>
<a class="sourceLine" id="cb30-103" data-line-number="103">  se1,</a>
<a class="sourceLine" id="cb30-104" data-line-number="104">  se2,</a>
<a class="sourceLine" id="cb30-105" data-line-number="105">  se3,</a>
<a class="sourceLine" id="cb30-106" data-line-number="106">  <span class="dt">ncol =</span> <span class="dv">1</span>,</a>
<a class="sourceLine" id="cb30-107" data-line-number="107">  <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">&quot;B.1&quot;</span>, <span class="st">&quot;B.2&quot;</span>, <span class="st">&quot;B.3&quot;</span>),</a>
<a class="sourceLine" id="cb30-108" data-line-number="108">  <span class="dt">align =</span> <span class="st">'hv'</span></a>
<a class="sourceLine" id="cb30-109" data-line-number="109">)</a>
<a class="sourceLine" id="cb30-110" data-line-number="110"></a>
<a class="sourceLine" id="cb30-111" data-line-number="111"></a>
<a class="sourceLine" id="cb30-112" data-line-number="112">reg_sent_df &lt;-<span class="st"> </span><span class="kw">get_model_data</span>(reg_sent, <span class="dt">sort.est =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb30-113" data-line-number="113"></a>
<a class="sourceLine" id="cb30-114" data-line-number="114">tlabels &lt;-</a>
<a class="sourceLine" id="cb30-115" data-line-number="115"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb30-116" data-line-number="116">    <span class="st">'Video date'</span>,</a>
<a class="sourceLine" id="cb30-117" data-line-number="117">    <span class="st">'Video views'</span>,</a>
<a class="sourceLine" id="cb30-118" data-line-number="118">    <span class="st">'Gender: female'</span>,</a>
<a class="sourceLine" id="cb30-119" data-line-number="119">    <span class="st">'Ethnicity: non-white'</span>,</a>
<a class="sourceLine" id="cb30-120" data-line-number="120">    <span class="kw">str_c</span>(<span class="st">'Topic: '</span>, topic_labels[<span class="dv">2</span><span class="op">:</span><span class="dv">30</span>])</a>
<a class="sourceLine" id="cb30-121" data-line-number="121">  )</a>
<a class="sourceLine" id="cb30-122" data-line-number="122"><span class="kw">names</span>(tlabels) &lt;-</a>
<a class="sourceLine" id="cb30-123" data-line-number="123"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb30-124" data-line-number="124">    <span class="st">'yt_date_num'</span>,</a>
<a class="sourceLine" id="cb30-125" data-line-number="125">    <span class="st">'yt_views'</span>,</a>
<a class="sourceLine" id="cb30-126" data-line-number="126">    <span class="st">'factor_femalefemale'</span>,</a>
<a class="sourceLine" id="cb30-127" data-line-number="127">    <span class="st">'factor_whitenon-white'</span>,</a>
<a class="sourceLine" id="cb30-128" data-line-number="128">    <span class="kw">str_c</span>(<span class="st">'T_'</span>, <span class="dv">2</span><span class="op">:</span><span class="dv">30</span>)</a>
<a class="sourceLine" id="cb30-129" data-line-number="129">  )</a>
<a class="sourceLine" id="cb30-130" data-line-number="130"></a>
<a class="sourceLine" id="cb30-131" data-line-number="131"></a>
<a class="sourceLine" id="cb30-132" data-line-number="132"><span class="co"># standardize coefficients</span></a>
<a class="sourceLine" id="cb30-133" data-line-number="133">df_std &lt;-<span class="st"> </span>df_reg <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span>sentiment) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb30-134" data-line-number="134"><span class="st">  </span><span class="kw">mutate_all</span>(arm<span class="op">::</span>rescale) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">sentiment =</span> df_reg<span class="op">$</span>sentiment)</a>
<a class="sourceLine" id="cb30-135" data-line-number="135">reg_sent_sd &lt;-<span class="st"> </span><span class="kw">glm</span>(sentiment <span class="op">~</span><span class="st">  </span>. , <span class="dt">data =</span> df_std)</a>
<a class="sourceLine" id="cb30-136" data-line-number="136"></a>
<a class="sourceLine" id="cb30-137" data-line-number="137"></a>
<a class="sourceLine" id="cb30-138" data-line-number="138">reg_sent_df_sd &lt;-<span class="st"> </span><span class="kw">get_model_data</span>(reg_sent_sd, <span class="dt">sort.est =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb30-139" data-line-number="139"></a>
<a class="sourceLine" id="cb30-140" data-line-number="140">tlabels &lt;-</a>
<a class="sourceLine" id="cb30-141" data-line-number="141"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb30-142" data-line-number="142">    <span class="st">'Video date'</span>,</a>
<a class="sourceLine" id="cb30-143" data-line-number="143">    <span class="st">'Video views'</span>,</a>
<a class="sourceLine" id="cb30-144" data-line-number="144">    <span class="st">'Gender: female'</span>,</a>
<a class="sourceLine" id="cb30-145" data-line-number="145">    <span class="st">'Ethnicity: non-white'</span>,</a>
<a class="sourceLine" id="cb30-146" data-line-number="146">    <span class="kw">str_c</span>(<span class="st">'Topic: '</span>, topic_labels[<span class="dv">2</span><span class="op">:</span><span class="dv">30</span>])</a>
<a class="sourceLine" id="cb30-147" data-line-number="147">  )</a>
<a class="sourceLine" id="cb30-148" data-line-number="148"><span class="kw">names</span>(tlabels) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'yt_date_num'</span>,</a>
<a class="sourceLine" id="cb30-149" data-line-number="149">                    <span class="st">'yt_views'</span>,</a>
<a class="sourceLine" id="cb30-150" data-line-number="150">                    <span class="st">'factor_female'</span>,</a>
<a class="sourceLine" id="cb30-151" data-line-number="151">                    <span class="st">'factor_white'</span>,</a>
<a class="sourceLine" id="cb30-152" data-line-number="152">                    <span class="kw">str_c</span>(<span class="st">'T_'</span>, <span class="dv">2</span><span class="op">:</span><span class="dv">30</span>))</a>
<a class="sourceLine" id="cb30-153" data-line-number="153"></a>
<a class="sourceLine" id="cb30-154" data-line-number="154"><span class="co"># create plot</span></a>
<a class="sourceLine" id="cb30-155" data-line-number="155">plot_sent_sd &lt;-</a>
<a class="sourceLine" id="cb30-156" data-line-number="156"><span class="st">  </span>reg_sent_df_sd <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb30-157" data-line-number="157">    <span class="dt">x =</span> estimate,</a>
<a class="sourceLine" id="cb30-158" data-line-number="158">    <span class="dt">y =</span> term,</a>
<a class="sourceLine" id="cb30-159" data-line-number="159">    <span class="dt">color =</span> group,</a>
<a class="sourceLine" id="cb30-160" data-line-number="160">    <span class="dt">shape =</span> group</a>
<a class="sourceLine" id="cb30-161" data-line-number="161">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-162" data-line-number="162"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span><span class="kw">scale_y_discrete</span>(<span class="dt">labels =</span> tlabels) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-163" data-line-number="163"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">5</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-164" data-line-number="164"><span class="st">  </span><span class="kw">geom_errorbarh</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb30-165" data-line-number="165">    <span class="dt">xmin =</span> conf.low,</a>
<a class="sourceLine" id="cb30-166" data-line-number="166">    <span class="dt">xmax =</span> conf.high,</a>
<a class="sourceLine" id="cb30-167" data-line-number="167">    <span class="dt">height =</span> <span class="dv">0</span></a>
<a class="sourceLine" id="cb30-168" data-line-number="168">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-169" data-line-number="169"><span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="dv">0</span>, <span class="dt">linetype =</span> <span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-170" data-line-number="170"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Standardized coefficients'</span>,</a>
<a class="sourceLine" id="cb30-171" data-line-number="171">       <span class="dt">y =</span> <span class="st">'Covariate'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-172" data-line-number="172"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">color =</span> <span class="ot">FALSE</span>, <span class="dt">shape =</span> <span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-173" data-line-number="173"><span class="st">  </span><span class="kw">scale_color_brewer</span>(<span class="dt">palette =</span> <span class="st">'Set1'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb30-174" data-line-number="174"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">face =</span></a>
<a class="sourceLine" id="cb30-175" data-line-number="175">                                     <span class="kw">c</span>(</a>
<a class="sourceLine" id="cb30-176" data-line-number="176">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">2</span>),</a>
<a class="sourceLine" id="cb30-177" data-line-number="177">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb30-178" data-line-number="178">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">9</span>),</a>
<a class="sourceLine" id="cb30-179" data-line-number="179">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb30-180" data-line-number="180">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">19</span>),</a>
<a class="sourceLine" id="cb30-181" data-line-number="181">                                       <span class="st">'bold'</span></a>
<a class="sourceLine" id="cb30-182" data-line-number="182">                                     )))</a>
<a class="sourceLine" id="cb30-183" data-line-number="183"></a>
<a class="sourceLine" id="cb30-184" data-line-number="184">cowplot<span class="op">::</span><span class="kw">plot_grid</span>(plot_sent_sd <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="ot">NULL</span>),</a>
<a class="sourceLine" id="cb30-185" data-line-number="185">          s_c2,</a>
<a class="sourceLine" id="cb30-186" data-line-number="186">          <span class="dt">ncol =</span> <span class="dv">2</span>,</a>
<a class="sourceLine" id="cb30-187" data-line-number="187">          <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'A'</span>, <span class="ot">NULL</span>))</a></code></pre></div>
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" width="768" /></p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/figure4.png'</span>, <span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">8</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a>
<a class="sourceLine" id="cb31-2" data-line-number="2"><span class="kw">ggsave</span>(<span class="st">'figures/figure4.pdf'</span>, <span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">8</span>)</a></code></pre></div>
</div>
<div id="sentiment-interaction-figure-s4" class="section level3">
<h3>Sentiment interaction (Figure S4)</h3>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb32-1" data-line-number="1">df_std2 &lt;-<span class="st"> </span>df_reg <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span>sentiment) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb32-2" data-line-number="2"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">dummy_white =</span> <span class="kw">ifelse</span>(factor_white <span class="op">==</span><span class="st"> 'white'</span>, <span class="dv">0</span>, <span class="dv">1</span>),</a>
<a class="sourceLine" id="cb32-3" data-line-number="3">         <span class="dt">sentiment =</span> df_reg<span class="op">$</span>sentiment) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb32-4" data-line-number="4"><span class="st">  </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span>factor_white)</a>
<a class="sourceLine" id="cb32-5" data-line-number="5"></a>
<a class="sourceLine" id="cb32-6" data-line-number="6"></a>
<a class="sourceLine" id="cb32-7" data-line-number="7">reg_sent_sd2 &lt;-<span class="st"> </span><span class="kw">glm</span>(sentiment <span class="op">~</span><span class="st">  </span>. <span class="op">+</span><span class="st"> </span>dummy_white <span class="op">*</span><span class="st"> </span>yt_date_num , <span class="dt">data =</span> df_std2)</a>
<a class="sourceLine" id="cb32-8" data-line-number="8"></a>
<a class="sourceLine" id="cb32-9" data-line-number="9"><span class="kw">plot_model</span>(reg_sent_sd2, <span class="dt">type =</span> <span class="st">'pred'</span>, </a>
<a class="sourceLine" id="cb32-10" data-line-number="10">           <span class="dt">terms =</span> <span class="kw">c</span>(<span class="st">'yt_date_num'</span>, <span class="st">'dummy_white'</span>)) <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb32-11" data-line-number="11"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n=</span> <span class="dv">5</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb32-12" data-line-number="12"><span class="st">  </span><span class="kw">scale_color_discrete</span>(<span class="dt">name =</span> <span class="st">'Ethnicity'</span>, <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'White'</span>, <span class="st">'Non-White'</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb32-13" data-line-number="13"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Date'</span>, <span class="dt">y =</span> <span class="st">'Sentiment'</span>, <span class="dt">title =</span> <span class="ot">NULL</span>)</a></code></pre></div>
<p><img 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width="864" /></p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb33-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/supporting_information_figure4.png'</span>,</a>
<a class="sourceLine" id="cb33-2" data-line-number="2">      <span class="dt">width =</span> <span class="dv">9</span>, <span class="dt">height =</span> <span class="dv">6</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a></code></pre></div>
</div>
<div id="youtube-dislikes-figure-s5" class="section level3">
<h3>YouTube Dislikes (Figure S5)</h3>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb34-1" data-line-number="1">df_reg2 &lt;-</a>
<a class="sourceLine" id="cb34-2" data-line-number="2"><span class="st">  </span>df <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(</a>
<a class="sourceLine" id="cb34-3" data-line-number="3">    yt_dislikes,</a>
<a class="sourceLine" id="cb34-4" data-line-number="4">    yt_date_num,</a>
<a class="sourceLine" id="cb34-5" data-line-number="5">    yt_views,</a>
<a class="sourceLine" id="cb34-6" data-line-number="6">    factor_female,</a>
<a class="sourceLine" id="cb34-7" data-line-number="7">    factor_white,</a>
<a class="sourceLine" id="cb34-8" data-line-number="8">    <span class="kw">starts_with</span>(<span class="st">'T_'</span>),</a>
<a class="sourceLine" id="cb34-9" data-line-number="9">    <span class="op">-</span>T_<span class="dv">1</span></a>
<a class="sourceLine" id="cb34-10" data-line-number="10">  )</a>
<a class="sourceLine" id="cb34-11" data-line-number="11"></a>
<a class="sourceLine" id="cb34-12" data-line-number="12"><span class="co"># dispersion test suggests to use negative binomial instead of poisson</span></a>
<a class="sourceLine" id="cb34-13" data-line-number="13"><span class="co"># for dislike counts</span></a>
<a class="sourceLine" id="cb34-14" data-line-number="14">AER<span class="op">::</span><span class="kw">dispersiontest</span>(<span class="kw">glm</span>(yt_dislikes <span class="op">~</span><span class="st">  </span>. , <span class="dt">data =</span> df_reg2, <span class="dt">family =</span> poisson),</a>
<a class="sourceLine" id="cb34-15" data-line-number="15">                    <span class="dt">trafo =</span> <span class="dv">1</span>)</a></code></pre></div>
<pre><code>## 
##  Overdispersion test
## 
## data:  glm(yt_dislikes ~ ., data = df_reg2, family = poisson)
## z = 7.4367, p-value = 0.00000000000005161
## alternative hypothesis: true alpha is greater than 0
## sample estimates:
##    alpha 
## 414.6048</code></pre>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb36-1" data-line-number="1">dreg2 &lt;-</a>
<a class="sourceLine" id="cb36-2" data-line-number="2"><span class="st">  </span><span class="kw">glm.nb</span>(yt_dislikes <span class="op">~</span><span class="st"> </span>yt_date_num  <span class="op">+</span><span class="st"> </span>yt_views ,  <span class="dt">data =</span> df_reg2)</a>
<a class="sourceLine" id="cb36-3" data-line-number="3"></a>
<a class="sourceLine" id="cb36-4" data-line-number="4">dreg3 &lt;-<span class="st"> </span><span class="kw">glm.nb</span>(yt_dislikes <span class="op">~</span><span class="st"> </span>yt_date_num <span class="op">+</span><span class="st"> </span>yt_views    <span class="op">+</span></a>
<a class="sourceLine" id="cb36-5" data-line-number="5"><span class="st">                  </span>factor_female <span class="op">+</span><span class="st"> </span>factor_white,</a>
<a class="sourceLine" id="cb36-6" data-line-number="6">                <span class="dt">data =</span> df_reg2)</a>
<a class="sourceLine" id="cb36-7" data-line-number="7"></a>
<a class="sourceLine" id="cb36-8" data-line-number="8">reg_dlikes &lt;-<span class="st"> </span><span class="kw">glm.nb</span>(yt_dislikes <span class="op">~</span><span class="st">  </span>. , <span class="dt">data =</span> df_reg2)</a>
<a class="sourceLine" id="cb36-9" data-line-number="9"></a>
<a class="sourceLine" id="cb36-10" data-line-number="10"></a>
<a class="sourceLine" id="cb36-11" data-line-number="11"></a>
<a class="sourceLine" id="cb36-12" data-line-number="12">dlikes_pred &lt;-<span class="st"> </span><span class="kw">ggeffect</span>(reg_dlikes, <span class="dt">x.as.factor =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb36-13" data-line-number="13"></a>
<a class="sourceLine" id="cb36-14" data-line-number="14"></a>
<a class="sourceLine" id="cb36-15" data-line-number="15"><span class="co"># STM variables</span></a>
<a class="sourceLine" id="cb36-16" data-line-number="16">s1 &lt;-<span class="st"> </span>dlikes_pred<span class="op">$</span>factor_female <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb36-17" data-line-number="17"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> x, <span class="dt">x =</span> predicted, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-18" data-line-number="18"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-19" data-line-number="19"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-20" data-line-number="20"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb36-21" data-line-number="21">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb36-22" data-line-number="22">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-23" data-line-number="23"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb36-24" data-line-number="24">    <span class="kw">aes</span>(<span class="dt">xmin =</span> conf.low, <span class="dt">xmax =</span> conf.high),</a>
<a class="sourceLine" id="cb36-25" data-line-number="25">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb36-26" data-line-number="26">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb36-27" data-line-number="27">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb36-28" data-line-number="28">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-29" data-line-number="29"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-30" data-line-number="30"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Gender'</span>,  <span class="dt">x =</span> <span class="st">'Video dislikes'</span>, <span class="dt">title =</span> <span class="st">''</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-31" data-line-number="31"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb36-32" data-line-number="32">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb36-33" data-line-number="33">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb36-34" data-line-number="34">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb36-35" data-line-number="35">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb36-36" data-line-number="36"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">5</span>))</a>
<a class="sourceLine" id="cb36-37" data-line-number="37"></a>
<a class="sourceLine" id="cb36-38" data-line-number="38">s2 &lt;-<span class="st"> </span>dlikes_pred<span class="op">$</span>factor_white <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb36-39" data-line-number="39"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">y =</span> x, <span class="dt">x =</span> predicted, <span class="dt">group =</span> <span class="dv">1</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-40" data-line-number="40"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">1.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-41" data-line-number="41"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>, <span class="dt">size =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-42" data-line-number="42"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb36-43" data-line-number="43">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb36-44" data-line-number="44">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-45" data-line-number="45"><span class="st">  </span><span class="kw">geom_errorbarh</span>(</a>
<a class="sourceLine" id="cb36-46" data-line-number="46">    <span class="kw">aes</span>(<span class="dt">xmin =</span> conf.low, <span class="dt">xmax =</span> conf.high),</a>
<a class="sourceLine" id="cb36-47" data-line-number="47">    <span class="dt">height =</span> <span class="fl">0.08</span>,</a>
<a class="sourceLine" id="cb36-48" data-line-number="48">    <span class="dt">color =</span> <span class="st">'#778899'</span>,</a>
<a class="sourceLine" id="cb36-49" data-line-number="49">    <span class="dt">size =</span> <span class="fl">0.5</span></a>
<a class="sourceLine" id="cb36-50" data-line-number="50">  ) <span class="op">+</span><span class="st"> </span><span class="kw">coord_flip</span>()  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-51" data-line-number="51"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>()) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-52" data-line-number="52"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Ethnicity'</span>,  <span class="dt">x =</span> <span class="st">'Video dislikes'</span>, <span class="dt">title =</span> <span class="st">''</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-53" data-line-number="53"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb36-54" data-line-number="54">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb36-55" data-line-number="55">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb36-56" data-line-number="56">    <span class="dt">panel.grid.major.x =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb36-57" data-line-number="57">  )   <span class="op">+</span></a>
<a class="sourceLine" id="cb36-58" data-line-number="58"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">5</span>))</a>
<a class="sourceLine" id="cb36-59" data-line-number="59"></a>
<a class="sourceLine" id="cb36-60" data-line-number="60">dlikes_pred &lt;-<span class="st"> </span><span class="kw">ggeffect</span>(reg_dlikes)</a>
<a class="sourceLine" id="cb36-61" data-line-number="61">s3 &lt;-<span class="st"> </span>dlikes_pred<span class="op">$</span>T_<span class="dv">4</span> <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb36-62" data-line-number="62"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> x, <span class="dt">y =</span> predicted)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-63" data-line-number="63"><span class="st">  </span><span class="kw">geom_line</span>(<span class="dt">color =</span> <span class="st">'#778899'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-64" data-line-number="64"><span class="st">  </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">ymin =</span> conf.low, <span class="dt">ymax =</span> conf.high),</a>
<a class="sourceLine" id="cb36-65" data-line-number="65">              <span class="dt">alpha =</span> <span class="fl">0.2</span>,</a>
<a class="sourceLine" id="cb36-66" data-line-number="66">              <span class="dt">fill =</span> <span class="st">'#778899'</span>)  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-67" data-line-number="67"><span class="st">  </span><span class="kw">scale_x_continuous</span>(</a>
<a class="sourceLine" id="cb36-68" data-line-number="68">    <span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">8</span>),</a>
<a class="sourceLine" id="cb36-69" data-line-number="69">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.00</span>, <span class="dv">0</span>),</a>
<a class="sourceLine" id="cb36-70" data-line-number="70">    <span class="dt">labels =</span> percent,</a>
<a class="sourceLine" id="cb36-71" data-line-number="71">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.6</span>)</a>
<a class="sourceLine" id="cb36-72" data-line-number="72">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-73" data-line-number="73"><span class="st">  </span><span class="kw">scale_y_continuous</span>(</a>
<a class="sourceLine" id="cb36-74" data-line-number="74">    <span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">7</span>),</a>
<a class="sourceLine" id="cb36-75" data-line-number="75">    <span class="dt">expand =</span> <span class="kw">c</span>(<span class="fl">0.00</span>, <span class="dv">0</span>),</a>
<a class="sourceLine" id="cb36-76" data-line-number="76">    <span class="dt">limits =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1600</span>)</a>
<a class="sourceLine" id="cb36-77" data-line-number="77">  ) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-78" data-line-number="78"><span class="st">  </span></a>
<a class="sourceLine" id="cb36-79" data-line-number="79"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">fill =</span> F,</a>
<a class="sourceLine" id="cb36-80" data-line-number="80">         <span class="dt">color =</span> F,</a>
<a class="sourceLine" id="cb36-81" data-line-number="81">         <span class="dt">group =</span> F)  <span class="op">+</span></a>
<a class="sourceLine" id="cb36-82" data-line-number="82"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="st">'Video dislikes'</span> , <span class="dt">x  =</span> <span class="st">'Topic proportion: inequality'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-83" data-line-number="83"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-84" data-line-number="84"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> pal) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-85" data-line-number="85"><span class="st">  </span><span class="kw">theme</span>(</a>
<a class="sourceLine" id="cb36-86" data-line-number="86">    <span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle =</span> <span class="dv">0</span>, <span class="dt">hjust =</span> <span class="dv">1</span>),</a>
<a class="sourceLine" id="cb36-87" data-line-number="87">    <span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb36-88" data-line-number="88">    <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>()</a>
<a class="sourceLine" id="cb36-89" data-line-number="89">  )</a>
<a class="sourceLine" id="cb36-90" data-line-number="90"></a>
<a class="sourceLine" id="cb36-91" data-line-number="91">s_c &lt;-<span class="st"> </span>cowplot<span class="op">::</span><span class="kw">plot_grid</span>(</a>
<a class="sourceLine" id="cb36-92" data-line-number="92">  s1,</a>
<a class="sourceLine" id="cb36-93" data-line-number="93">  s2,</a>
<a class="sourceLine" id="cb36-94" data-line-number="94">  s3,</a>
<a class="sourceLine" id="cb36-95" data-line-number="95">  <span class="dt">align =</span> <span class="st">'hv'</span>,</a>
<a class="sourceLine" id="cb36-96" data-line-number="96">  <span class="dt">ncol =</span> <span class="dv">1</span>,</a>
<a class="sourceLine" id="cb36-97" data-line-number="97">  <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'B.1'</span>, <span class="st">'B.2'</span>, <span class="st">'B.3'</span>)</a>
<a class="sourceLine" id="cb36-98" data-line-number="98">)</a>
<a class="sourceLine" id="cb36-99" data-line-number="99"></a>
<a class="sourceLine" id="cb36-100" data-line-number="100"></a>
<a class="sourceLine" id="cb36-101" data-line-number="101">reg_dlikes_df &lt;-<span class="st"> </span><span class="kw">get_model_data</span>(reg_dlikes, <span class="dt">sort.est =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb36-102" data-line-number="102">                                <span class="dt">transform =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb36-103" data-line-number="103"></a>
<a class="sourceLine" id="cb36-104" data-line-number="104">tlabels &lt;-</a>
<a class="sourceLine" id="cb36-105" data-line-number="105"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb36-106" data-line-number="106">    <span class="st">'Video date'</span>,</a>
<a class="sourceLine" id="cb36-107" data-line-number="107">    <span class="st">'Video views'</span>,</a>
<a class="sourceLine" id="cb36-108" data-line-number="108">    <span class="st">'Gender: female'</span>,</a>
<a class="sourceLine" id="cb36-109" data-line-number="109">    <span class="st">'Ethnicity: non-white'</span>,</a>
<a class="sourceLine" id="cb36-110" data-line-number="110">    <span class="kw">str_c</span>(<span class="st">'Topic: '</span>, topic_labels[<span class="dv">2</span><span class="op">:</span><span class="dv">30</span>])</a>
<a class="sourceLine" id="cb36-111" data-line-number="111">  )</a>
<a class="sourceLine" id="cb36-112" data-line-number="112"><span class="kw">names</span>(tlabels) &lt;-</a>
<a class="sourceLine" id="cb36-113" data-line-number="113"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb36-114" data-line-number="114">    <span class="st">'yt_date_num'</span>,</a>
<a class="sourceLine" id="cb36-115" data-line-number="115">    <span class="st">'yt_views'</span>,</a>
<a class="sourceLine" id="cb36-116" data-line-number="116">    <span class="st">'factor_femalefemale'</span>,</a>
<a class="sourceLine" id="cb36-117" data-line-number="117">    <span class="st">'factor_whitenon-white'</span>,</a>
<a class="sourceLine" id="cb36-118" data-line-number="118">    <span class="kw">str_c</span>(<span class="st">'T_'</span>, <span class="dv">2</span><span class="op">:</span><span class="dv">30</span>)</a>
<a class="sourceLine" id="cb36-119" data-line-number="119">  )</a>
<a class="sourceLine" id="cb36-120" data-line-number="120"></a>
<a class="sourceLine" id="cb36-121" data-line-number="121">plot_dlike &lt;-</a>
<a class="sourceLine" id="cb36-122" data-line-number="122"><span class="st">  </span>reg_dlikes_df <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> estimate, <span class="dt">y =</span> term, <span class="dt">color =</span> group)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-123" data-line-number="123"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span><span class="kw">scale_y_discrete</span>(<span class="dt">labels =</span> tlabels) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-124" data-line-number="124"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">6</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-125" data-line-number="125"><span class="st">  </span><span class="kw">geom_errorbarh</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb36-126" data-line-number="126">    <span class="dt">xmin =</span> conf.low,</a>
<a class="sourceLine" id="cb36-127" data-line-number="127">    <span class="dt">xmax =</span> conf.high,</a>
<a class="sourceLine" id="cb36-128" data-line-number="128">    <span class="dt">height =</span> <span class="dv">0</span></a>
<a class="sourceLine" id="cb36-129" data-line-number="129">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-130" data-line-number="130"><span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="dv">0</span>, <span class="dt">linetype =</span> <span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-131" data-line-number="131"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Coefficient: YouTube video dislikes'</span>, <span class="dt">y =</span> <span class="st">'Covariate'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-132" data-line-number="132"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">color =</span> <span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-133" data-line-number="133"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">'#377eb8'</span>, <span class="st">'#e41a1c'</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-134" data-line-number="134"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">face =</span></a>
<a class="sourceLine" id="cb36-135" data-line-number="135">                                     <span class="kw">c</span>(</a>
<a class="sourceLine" id="cb36-136" data-line-number="136">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">18</span>),</a>
<a class="sourceLine" id="cb36-137" data-line-number="137">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb36-138" data-line-number="138">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">7</span>),</a>
<a class="sourceLine" id="cb36-139" data-line-number="139">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb36-140" data-line-number="140">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">5</span>),</a>
<a class="sourceLine" id="cb36-141" data-line-number="141">                                       <span class="st">'bold'</span></a>
<a class="sourceLine" id="cb36-142" data-line-number="142">                                     )))</a>
<a class="sourceLine" id="cb36-143" data-line-number="143"></a>
<a class="sourceLine" id="cb36-144" data-line-number="144"><span class="co"># standardize coefficients</span></a>
<a class="sourceLine" id="cb36-145" data-line-number="145">df_std2 &lt;-<span class="st"> </span>df_reg2 <span class="op">%&gt;%</span><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span>yt_dislikes) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb36-146" data-line-number="146"><span class="st">  </span><span class="kw">mutate_all</span>(arm<span class="op">::</span>rescale) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">yt_dislikes =</span> df_reg2<span class="op">$</span>yt_dislikes)</a>
<a class="sourceLine" id="cb36-147" data-line-number="147">reg_dlikes_sd &lt;-<span class="st"> </span><span class="kw">glm.nb</span>(yt_dislikes <span class="op">~</span><span class="st">  </span>. , <span class="dt">data =</span> df_std2)</a>
<a class="sourceLine" id="cb36-148" data-line-number="148"></a>
<a class="sourceLine" id="cb36-149" data-line-number="149"></a>
<a class="sourceLine" id="cb36-150" data-line-number="150"></a>
<a class="sourceLine" id="cb36-151" data-line-number="151">reg_dlikes_df_sd &lt;-<span class="st"> </span><span class="kw">get_model_data</span>(reg_dlikes_sd, <span class="dt">sort.est =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb36-152" data-line-number="152">                                   <span class="dt">transform =</span> <span class="ot">NULL</span>)</a>
<a class="sourceLine" id="cb36-153" data-line-number="153"></a>
<a class="sourceLine" id="cb36-154" data-line-number="154">tlabels &lt;-</a>
<a class="sourceLine" id="cb36-155" data-line-number="155"><span class="st">  </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb36-156" data-line-number="156">    <span class="st">'Video date'</span>,</a>
<a class="sourceLine" id="cb36-157" data-line-number="157">    <span class="st">'Video views'</span>,</a>
<a class="sourceLine" id="cb36-158" data-line-number="158">    <span class="st">'Gender: female'</span>,</a>
<a class="sourceLine" id="cb36-159" data-line-number="159">    <span class="st">'Ethnicity: non-white'</span>,</a>
<a class="sourceLine" id="cb36-160" data-line-number="160">    <span class="kw">str_c</span>(<span class="st">'Topic: '</span>, topic_labels[<span class="dv">2</span><span class="op">:</span><span class="dv">30</span>])</a>
<a class="sourceLine" id="cb36-161" data-line-number="161">  )</a>
<a class="sourceLine" id="cb36-162" data-line-number="162"><span class="kw">names</span>(tlabels) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">'yt_date_num'</span>,</a>
<a class="sourceLine" id="cb36-163" data-line-number="163">                    <span class="st">'yt_views'</span>,</a>
<a class="sourceLine" id="cb36-164" data-line-number="164">                    <span class="st">'factor_female'</span>,</a>
<a class="sourceLine" id="cb36-165" data-line-number="165">                    <span class="st">'factor_white'</span>,</a>
<a class="sourceLine" id="cb36-166" data-line-number="166">                    <span class="kw">str_c</span>(<span class="st">'T_'</span>, <span class="dv">2</span><span class="op">:</span><span class="dv">30</span>))</a>
<a class="sourceLine" id="cb36-167" data-line-number="167"></a>
<a class="sourceLine" id="cb36-168" data-line-number="168"></a>
<a class="sourceLine" id="cb36-169" data-line-number="169"><span class="co"># create visualization</span></a>
<a class="sourceLine" id="cb36-170" data-line-number="170">plot_dlikes_sd &lt;-</a>
<a class="sourceLine" id="cb36-171" data-line-number="171"><span class="st">  </span>reg_dlikes_df_sd <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb36-172" data-line-number="172">    <span class="dt">x =</span> estimate,</a>
<a class="sourceLine" id="cb36-173" data-line-number="173">    <span class="dt">y =</span> term,</a>
<a class="sourceLine" id="cb36-174" data-line-number="174">    <span class="dt">color =</span> group,</a>
<a class="sourceLine" id="cb36-175" data-line-number="175">    <span class="dt">shape =</span> group</a>
<a class="sourceLine" id="cb36-176" data-line-number="176">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-177" data-line-number="177"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">2</span>) <span class="op">+</span><span class="st"> </span><span class="kw">scale_y_discrete</span>(<span class="dt">labels =</span> tlabels) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-178" data-line-number="178"><span class="st">  </span><span class="kw">scale_x_continuous</span>(<span class="dt">breaks =</span> <span class="kw">pretty_breaks</span>(<span class="dt">n =</span> <span class="dv">7</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-179" data-line-number="179"><span class="st">  </span><span class="kw">geom_errorbarh</span>(<span class="kw">aes</span>(</a>
<a class="sourceLine" id="cb36-180" data-line-number="180">    <span class="dt">xmin =</span> conf.low,</a>
<a class="sourceLine" id="cb36-181" data-line-number="181">    <span class="dt">xmax =</span> conf.high,</a>
<a class="sourceLine" id="cb36-182" data-line-number="182">    <span class="dt">height =</span> <span class="dv">0</span></a>
<a class="sourceLine" id="cb36-183" data-line-number="183">  )) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-184" data-line-number="184"><span class="st">  </span><span class="kw">geom_vline</span>(<span class="dt">xintercept =</span> <span class="dv">0</span>, <span class="dt">linetype =</span> <span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-185" data-line-number="185"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">'Standardized coefficients'</span>,</a>
<a class="sourceLine" id="cb36-186" data-line-number="186">       <span class="dt">y =</span> <span class="st">'Covariate'</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-187" data-line-number="187"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">color =</span> <span class="ot">FALSE</span>, <span class="dt">shape =</span> <span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-188" data-line-number="188"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">'#377eb8'</span>, <span class="st">'#e41a1c'</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb36-189" data-line-number="189"><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text.y =</span> <span class="kw">element_text</span>(<span class="dt">face =</span></a>
<a class="sourceLine" id="cb36-190" data-line-number="190">                                     <span class="kw">c</span>(</a>
<a class="sourceLine" id="cb36-191" data-line-number="191">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">10</span>),</a>
<a class="sourceLine" id="cb36-192" data-line-number="192">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb36-193" data-line-number="193">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">17</span>),</a>
<a class="sourceLine" id="cb36-194" data-line-number="194">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb36-195" data-line-number="195">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">1</span>),</a>
<a class="sourceLine" id="cb36-196" data-line-number="196">                                       <span class="st">'bold'</span>,</a>
<a class="sourceLine" id="cb36-197" data-line-number="197">                                       <span class="kw">rep</span>(<span class="st">'plain'</span>, <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb36-198" data-line-number="198">                                     )))</a>
<a class="sourceLine" id="cb36-199" data-line-number="199"></a>
<a class="sourceLine" id="cb36-200" data-line-number="200"></a>
<a class="sourceLine" id="cb36-201" data-line-number="201">cowplot<span class="op">::</span><span class="kw">plot_grid</span>(plot_dlikes_sd <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">y =</span> <span class="ot">NULL</span>),</a>
<a class="sourceLine" id="cb36-202" data-line-number="202">                   s_c,</a>
<a class="sourceLine" id="cb36-203" data-line-number="203">                   <span class="dt">ncol =</span> <span class="dv">2</span>,</a>
<a class="sourceLine" id="cb36-204" data-line-number="204">                   <span class="dt">labels =</span> <span class="kw">c</span>(<span class="st">'A'</span>, <span class="ot">NULL</span>))</a></code></pre></div>
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" width="768" /></p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb37-1" data-line-number="1"><span class="kw">ggsave</span>(<span class="st">'figures/supporting_information_figure5.png'</span>, </a>
<a class="sourceLine" id="cb37-2" data-line-number="2">       <span class="dt">width =</span> <span class="dv">8</span>, <span class="dt">height =</span> <span class="dv">8</span>, <span class="dt">dpi =</span> <span class="dv">300</span>)</a></code></pre></div>
</div>
<div id="regression-tables-s3" class="section level3">
<h3>Regression tables (S3)</h3>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb38-1" data-line-number="1"><span class="kw">texreg</span>(</a>
<a class="sourceLine" id="cb38-2" data-line-number="2">  <span class="kw">list</span>(reg_sent, reg_dlikes),</a>
<a class="sourceLine" id="cb38-3" data-line-number="3">  <span class="dt">dcolumn =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb38-4" data-line-number="4">  <span class="dt">booktabs =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb38-5" data-line-number="5">  <span class="dt">digits =</span> <span class="dv">2</span>,</a>
<a class="sourceLine" id="cb38-6" data-line-number="6">  <span class="dt">custom.model.names =</span> <span class="kw">c</span>(<span class="st">'Comment sentiment'</span>, <span class="st">'Video dislikes'</span>),</a>
<a class="sourceLine" id="cb38-7" data-line-number="7">  <span class="dt">custom.coef.names =</span> <span class="kw">c</span>(<span class="st">'Intercept'</span>,</a>
<a class="sourceLine" id="cb38-8" data-line-number="8">                        tlabels <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">str_replace</span>(<span class="st">'&amp;'</span>, <span class="st">'and'</span>)),</a>
<a class="sourceLine" id="cb38-9" data-line-number="9">  <span class="dt">single.row =</span> <span class="ot">TRUE</span>,</a>
<a class="sourceLine" id="cb38-10" data-line-number="10">  <span class="dt">use.packages =</span> <span class="ot">FALSE</span>,</a>
<a class="sourceLine" id="cb38-11" data-line-number="11">  <span class="dt">label =</span> <span class="st">&quot;tab:2&quot;</span>,</a>
<a class="sourceLine" id="cb38-12" data-line-number="12">  <span class="dt">caption =</span> <span class="st">&quot;Regression models for comment sentiment and video dislikes of TED talks on YouTube&quot;</span>,</a>
<a class="sourceLine" id="cb38-13" data-line-number="13">  <span class="dt">float.pos =</span> <span class="st">&quot;H&quot;</span>,</a>
<a class="sourceLine" id="cb38-14" data-line-number="14">  <span class="dt">file =</span> <span class="st">'figures/regression_tables.tex'</span></a>
<a class="sourceLine" id="cb38-15" data-line-number="15">)       </a></code></pre></div>
</div>
</div>
<div id="supporting-information-s1---validation-of-image-recognition-annotations" class="section level2">
<h2>Supporting Information S1 - Validation of image recognition annotations</h2>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb39-1" data-line-number="1"><span class="co"># read file for human codings and image recognition annotations</span></a>
<a class="sourceLine" id="cb39-2" data-line-number="2">comb &lt;-<span class="st"> </span><span class="kw">read_tsv</span>(<span class="st">'ted_talks_validation.tsv'</span>)</a></code></pre></div>
<pre><code>## Parsed with column specification:
## cols(
##   speaker = col_character(),
##   speaker_ethnicity = col_character(),
##   speaker_face_gender = col_character(),
##   speaker_image = col_character(),
##   speaker_ethnicity2 = col_character(),
##   speaker_face_gender2 = col_character(),
##   speaker_ethnicity1 = col_character(),
##   speaker_face_gender1 = col_character()
## )</code></pre>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb41-1" data-line-number="1"><span class="kw">head</span>(comb)</a></code></pre></div>
<pre><code>## # A tibble: 6 x 8
##   speaker speaker_ethnici… speaker_face_ge… speaker_image speaker_ethnici…
##   &lt;chr&gt;   &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;         &lt;chr&gt;           
## 1 Shane … White            male             dc91c197beb7… White           
## 2 Michae… White            male             4783f0841cf2… White           
## 3 David … White            male             20321.jpg     White           
## 4 Paul S… White            male             fdb329dbac36… White           
## 5 Sean C… White            male             152999.jpg    White           
## 6 Bruce … White            male             871cd3c165a4… White           
## # … with 3 more variables: speaker_face_gender2 &lt;chr&gt;,
## #   speaker_ethnicity1 &lt;chr&gt;, speaker_face_gender1 &lt;chr&gt;</code></pre>
<p><a href="https://en.wikipedia.org/wiki/Cohen%27s_kappa">Cohen’s Kappa</a> will be used to determine the interrater agreement between human coders and the <a href="https://www.kairos.com/docs/api/">Kairos face recognition algorithm</a>.</p>
<div id="pairwise-agreement-ethnicity" class="section level3">
<h3>Pairwise Agreement: Ethnicity</h3>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb43-1" data-line-number="1">eth_kappa_<span class="dv">1</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_ethnicity1, speaker_ethnicity) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb43-2" data-line-number="2"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb43-3" data-line-number="3">eth_kappa_<span class="dv">2</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_ethnicity2, speaker_ethnicity) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb43-4" data-line-number="4"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb43-5" data-line-number="5">eth_kappa_<span class="dv">3</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_ethnicity2, speaker_ethnicity1) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb43-6" data-line-number="6"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb43-7" data-line-number="7"></a>
<a class="sourceLine" id="cb43-8" data-line-number="8">eth_kappa_<span class="dv">1</span>  <span class="co"># rater 1 / algorithm</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.833 
## 
##         z = 18.2 
##   p-value = 0 
## 
##           Kappa      z p.value
## Asian     1.000 14.142   0.000
## Black     0.949 13.420   0.000
## Hispanic  0.468  6.620   0.000
## Other     0.590  8.340   0.000
## White     0.867 12.257   0.000</code></pre>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb45-1" data-line-number="1">eth_kappa_<span class="dv">2</span>  <span class="co"># rater 2 / algorithm</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.888 
## 
##         z = 19.1 
##   p-value = 0 
## 
##           Kappa      z p.value
## Asian     0.895 12.653   0.000
## Black     0.974 13.774   0.000
## Hispanic  0.734 10.381   0.000
## Other     0.745 10.534   0.000
## White     0.903 12.776   0.000</code></pre>
<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb47-1" data-line-number="1">eth_kappa_<span class="dv">3</span>  <span class="co"># rater 1 / rater 2</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.825 
## 
##         z = 17.9 
##   p-value = 0 
## 
##           Kappa      z p.value
## Asian     0.895 12.653   0.000
## Black     0.974 13.774   0.000
## Hispanic  0.519  7.340   0.000
## Other     0.795 11.241   0.000
## White     0.821 11.604   0.000</code></pre>
<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb49-1" data-line-number="1"><span class="co"># raters / machine average</span></a>
<a class="sourceLine" id="cb49-2" data-line-number="2"><span class="kw">mean</span>(<span class="kw">c</span>(eth_kappa_<span class="dv">1</span><span class="op">$</span>value, eth_kappa_<span class="dv">2</span><span class="op">$</span>value))</a></code></pre></div>
<pre><code>## [1] 0.8601235</code></pre>
</div>
<div id="pairwise-agreement-gender" class="section level3">
<h3>Pairwise Agreement: Gender</h3>
<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb51-1" data-line-number="1">gender_kappa_<span class="dv">1</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_face_gender1, speaker_face_gender) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb51-2" data-line-number="2"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb51-3" data-line-number="3">gender_kappa_<span class="dv">2</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_face_gender2, speaker_face_gender) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb51-4" data-line-number="4"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb51-5" data-line-number="5">gender_kappa_<span class="dv">3</span> &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker_face_gender2, speaker_face_gender1) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb51-6" data-line-number="6"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb51-7" data-line-number="7"></a>
<a class="sourceLine" id="cb51-8" data-line-number="8">gender_kappa_<span class="dv">1</span> <span class="co"># rater 1 / algorithm</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.946 
## 
##         z = 13.4 
##   p-value = 0 
## 
##         Kappa      z p.value
## female  0.946 13.382   0.000
## male    0.946 13.382   0.000</code></pre>
<div class="sourceCode" id="cb53"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb53-1" data-line-number="1">gender_kappa_<span class="dv">2</span> <span class="co"># rater 2 / algorithm</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.968 
## 
##         z = 13.7 
##   p-value = 0 
## 
##         Kappa      z p.value
## female  0.968 13.686   0.000
## male    0.968 13.686   0.000</code></pre>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb55-1" data-line-number="1">gender_kappa_<span class="dv">3</span> <span class="co"># rater 1 / rater 2</span></a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 2 
##     Kappa = 0.957 
## 
##         z = 13.5 
##   p-value = 0 
## 
##         Kappa      z p.value
## female  0.957 13.535   0.000
## male    0.957 13.535   0.000</code></pre>
<div class="sourceCode" id="cb57"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb57-1" data-line-number="1"><span class="co"># raters / machine average</span></a>
<a class="sourceLine" id="cb57-2" data-line-number="2"><span class="kw">mean</span>(<span class="kw">c</span>(gender_kappa_<span class="dv">1</span><span class="op">$</span>value, gender_kappa_<span class="dv">2</span><span class="op">$</span>value))</a></code></pre></div>
<pre><code>## [1] 0.9569788</code></pre>
</div>
<div id="overall-agreement-ethnicity" class="section level3">
<h3>Overall Agreement: Ethnicity</h3>
<div class="sourceCode" id="cb59"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb59-1" data-line-number="1">eth_all &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(<span class="kw">contains</span>(<span class="st">'speaker_ethnicity'</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb59-2" data-line-number="2"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">exact =</span> <span class="ot">FALSE</span>, <span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb59-3" data-line-number="3">eth_all</a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 3 
##     Kappa = 0.848 
## 
##         z = 31.9 
##   p-value = 0 
## 
##           Kappa      z p.value
## Asian     0.932 22.828   0.000
## Black     0.965 23.650   0.000
## Hispanic  0.575 14.090   0.000
## Other     0.707 17.329   0.000
## White     0.864 21.153   0.000</code></pre>
</div>
<div id="overall-agreement-gender" class="section level3">
<h3>Overall Agreement: Gender</h3>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb61-1" data-line-number="1">sex_all &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(<span class="kw">contains</span>(<span class="st">'speaker_face'</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb61-2" data-line-number="2"><span class="st">  </span><span class="kw">kappam.fleiss</span>(<span class="dt">exact =</span> <span class="ot">FALSE</span>, <span class="dt">detail =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb61-3" data-line-number="3">sex_all</a></code></pre></div>
<pre><code>##  Fleiss' Kappa for m Raters
## 
##  Subjects = 200 
##    Raters = 3 
##     Kappa = 0.957 
## 
##         z = 23.4 
##   p-value = 0 
## 
##         Kappa      z p.value
## female  0.957 23.442   0.000
## male    0.957 23.442   0.000</code></pre>
</div>
<div id="disagreements" class="section level3">
<h3>Disagreements</h3>
<div id="rater-1" class="section level4">
<h4>Rater 1</h4>
<div class="sourceCode" id="cb63"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb63-1" data-line-number="1">comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker, speaker_face_gender, speaker_face_gender1,</a>
<a class="sourceLine" id="cb63-2" data-line-number="2">                speaker_ethnicity, speaker_ethnicity1 ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb63-3" data-line-number="3"><span class="st">  </span><span class="kw">filter</span>(</a>
<a class="sourceLine" id="cb63-4" data-line-number="4">            speaker_ethnicity <span class="op">!=</span><span class="st"> </span>speaker_ethnicity1)  <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb63-5" data-line-number="5"><span class="st">  </span><span class="kw">arrange</span>(speaker) </a></code></pre></div>
<pre><code>## # A tibble: 14 x 5
##    speaker speaker_face_ge… speaker_face_ge… speaker_ethnici…
##    &lt;chr&gt;   &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;           
##  1 Bruno … male             male             White           
##  2 David … male             male             Hispanic        
##  3 eL Seed male             male             White           
##  4 Marcin… male             male             Hispanic        
##  5 Martin… male             male             White           
##  6 May El… female           female           Hispanic        
##  7 Pearl … female           female           White           
##  8 Ravin … male             male             Hispanic        
##  9 Rob Re… male             male             Hispanic        
## 10 Salil … male             male             White           
## 11 Sanjay… male             male             Other           
## 12 Sonaar… male             male             Hispanic        
## 13 Sunith… female           female           Black           
## 14 Trevor… male             male             Hispanic        
## # … with 1 more variable: speaker_ethnicity1 &lt;chr&gt;</code></pre>
</div>
<div id="rater-2" class="section level4">
<h4>Rater 2</h4>
<div class="sourceCode" id="cb65"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb65-1" data-line-number="1">comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(speaker, speaker_face_gender, speaker_face_gender2,</a>
<a class="sourceLine" id="cb65-2" data-line-number="2">                speaker_ethnicity, speaker_ethnicity2) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb65-3" data-line-number="3"><span class="st">  </span><span class="kw">filter</span>(</a>
<a class="sourceLine" id="cb65-4" data-line-number="4">            speaker_ethnicity <span class="op">!=</span><span class="st"> </span>speaker_ethnicity2) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb65-5" data-line-number="5"><span class="st">  </span><span class="kw">arrange</span>(speaker) </a></code></pre></div>
<pre><code>## # A tibble: 9 x 5
##   speaker speaker_face_ge… speaker_face_ge… speaker_ethnici…
##   &lt;chr&gt;   &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;           
## 1 David … male             male             Hispanic        
## 2 Fei-Fe… female           female           Asian           
## 3 Iwan B… male             male             Hispanic        
## 4 Mechai… male             male             Asian           
## 5 Michae… male             male             White           
## 6 Rob Re… male             male             Hispanic        
## 7 Sanjay… male             male             Other           
## 8 Sonaar… male             male             Hispanic        
## 9 Sunith… female           female           Black           
## # … with 1 more variable: speaker_ethnicity2 &lt;chr&gt;</code></pre>
</div>
<div id="examples" class="section level4">
<h4>Examples</h4>
<div class="sourceCode" id="cb67"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb67-1" data-line-number="1">comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(speaker <span class="op">%in%</span></a>
<a class="sourceLine" id="cb67-2" data-line-number="2"><span class="kw">c</span>(<span class="st">'Danielle Feinberg'</span>, <span class="st">'David Pogue'</span>,</a>
<a class="sourceLine" id="cb67-3" data-line-number="3">  <span class="st">'Sonaar Luthra'</span>, <span class="st">'Sunitha Krishnan'</span> )) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb67-4" data-line-number="4"><span class="st">  </span><span class="kw">select</span>(<span class="kw">sort</span>(<span class="kw">current_vars</span>()))  </a></code></pre></div>
<pre><code>## Warning: current_vars() is deprecated. 
## Please use tidyselect::peek_vars() instead
## This warning is displayed once per session.</code></pre>
<pre><code>## # A tibble: 4 x 8
##   speaker speaker_ethnici… speaker_ethnici… speaker_ethnici…
##   &lt;chr&gt;   &lt;chr&gt;            &lt;chr&gt;            &lt;chr&gt;           
## 1 Sunith… Black            Other            Other           
## 2 Sonaar… Hispanic         Other            White           
## 3 David … Hispanic         White            White           
## 4 Daniel… White            White            White           
## # … with 4 more variables: speaker_face_gender &lt;chr&gt;,
## #   speaker_face_gender1 &lt;chr&gt;, speaker_face_gender2 &lt;chr&gt;,
## #   speaker_image &lt;chr&gt;</code></pre>
</div>
</div>
<div id="calculate-accuracy-and-f1-scores" class="section level3">
<h3>Calculate Accuracy and F1 Scores</h3>
<div id="ethnicity-1" class="section level4">
<h4>Ethnicity</h4>
<p>Calculating precision, recall and F1 scores for multiclass applications (such as ethnicity in our case) is more complicated then for binary prediction tasks. See <a href="https://www.datascienceblog.net/post/machine-learning/performance-measures-multi-class-problems/">this source</a> for a thorough explanation. Most of the code below is based upon the source.</p>
<div class="sourceCode" id="cb70"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb70-1" data-line-number="1">get.conf.stats &lt;-<span class="st"> </span><span class="cf">function</span>(cm) {</a>
<a class="sourceLine" id="cb70-2" data-line-number="2">    out &lt;-<span class="st"> </span><span class="kw">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="kw">length</span>(cm))</a>
<a class="sourceLine" id="cb70-3" data-line-number="3">    <span class="cf">for</span> (i <span class="cf">in</span> <span class="kw">seq_along</span>(cm)) {</a>
<a class="sourceLine" id="cb70-4" data-line-number="4">        x &lt;-<span class="st"> </span>cm[[i]]</a>
<a class="sourceLine" id="cb70-5" data-line-number="5">        tp &lt;-<span class="st"> </span>x<span class="op">$</span>table[x<span class="op">$</span>positive, x<span class="op">$</span>positive] </a>
<a class="sourceLine" id="cb70-6" data-line-number="6">        fp &lt;-<span class="st"> </span><span class="kw">sum</span>(x<span class="op">$</span>table[x<span class="op">$</span>positive, <span class="kw">colnames</span>(x<span class="op">$</span>table) <span class="op">!=</span><span class="st"> </span>x<span class="op">$</span>positive])</a>
<a class="sourceLine" id="cb70-7" data-line-number="7">        fn &lt;-<span class="st"> </span><span class="kw">sum</span>(x<span class="op">$</span>table[<span class="kw">colnames</span>(x<span class="op">$</span>table) <span class="op">!=</span><span class="st"> </span>x<span class="op">$</span>positie, x<span class="op">$</span>positive])</a>
<a class="sourceLine" id="cb70-8" data-line-number="8">        elem &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="dt">tp =</span> tp, <span class="dt">fp =</span> fp, <span class="dt">fn =</span> fn)</a>
<a class="sourceLine" id="cb70-9" data-line-number="9">        out[[i]] &lt;-<span class="st"> </span>elem</a>
<a class="sourceLine" id="cb70-10" data-line-number="10">    }</a>
<a class="sourceLine" id="cb70-11" data-line-number="11">    df &lt;-<span class="st"> </span><span class="kw">do.call</span>(rbind, out)</a>
<a class="sourceLine" id="cb70-12" data-line-number="12">    <span class="kw">rownames</span>(df) &lt;-<span class="st"> </span><span class="kw">unlist</span>(<span class="kw">lapply</span>(cm, <span class="cf">function</span>(x) x<span class="op">$</span>positive))</a>
<a class="sourceLine" id="cb70-13" data-line-number="13">    <span class="kw">return</span>(<span class="kw">as.data.frame</span>(df))</a>
<a class="sourceLine" id="cb70-14" data-line-number="14">}</a>
<a class="sourceLine" id="cb70-15" data-line-number="15">get.micro.f1 &lt;-<span class="st"> </span><span class="cf">function</span>(cm) {</a>
<a class="sourceLine" id="cb70-16" data-line-number="16">    cm.summary &lt;-<span class="st"> </span><span class="kw">get.conf.stats</span>(cm)</a>
<a class="sourceLine" id="cb70-17" data-line-number="17">    tp &lt;-<span class="st"> </span><span class="kw">sum</span>(cm.summary<span class="op">$</span>tp)</a>
<a class="sourceLine" id="cb70-18" data-line-number="18">    fn &lt;-<span class="st"> </span><span class="kw">sum</span>(cm.summary<span class="op">$</span>fn)</a>
<a class="sourceLine" id="cb70-19" data-line-number="19">    fp &lt;-<span class="st"> </span><span class="kw">sum</span>(cm.summary<span class="op">$</span>fp)</a>
<a class="sourceLine" id="cb70-20" data-line-number="20">    pr &lt;-<span class="st"> </span>tp <span class="op">/</span><span class="st"> </span>(tp <span class="op">+</span><span class="st"> </span>fp)</a>
<a class="sourceLine" id="cb70-21" data-line-number="21">    re &lt;-<span class="st"> </span>tp <span class="op">/</span><span class="st"> </span>(tp <span class="op">+</span><span class="st"> </span>fn)</a>
<a class="sourceLine" id="cb70-22" data-line-number="22">    f1 &lt;-<span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span>((pr <span class="op">*</span><span class="st"> </span>re) <span class="op">/</span><span class="st"> </span>(pr <span class="op">+</span><span class="st"> </span>re))</a>
<a class="sourceLine" id="cb70-23" data-line-number="23">    <span class="kw">return</span>(f1)</a>
<a class="sourceLine" id="cb70-24" data-line-number="24">}</a>
<a class="sourceLine" id="cb70-25" data-line-number="25"></a>
<a class="sourceLine" id="cb70-26" data-line-number="26">get.macro.f1 &lt;-<span class="st"> </span><span class="cf">function</span>(cm) {</a>
<a class="sourceLine" id="cb70-27" data-line-number="27">    c &lt;-<span class="st"> </span>cm[[<span class="dv">1</span>]]<span class="op">$</span>byClass <span class="co"># a single matrix is sufficient</span></a>
<a class="sourceLine" id="cb70-28" data-line-number="28">    re &lt;-<span class="st"> </span><span class="kw">sum</span>(c[, <span class="st">&quot;Recall&quot;</span>]) <span class="op">/</span><span class="st"> </span><span class="kw">nrow</span>(c)</a>
<a class="sourceLine" id="cb70-29" data-line-number="29">    pr &lt;-<span class="st"> </span><span class="kw">sum</span>(c[, <span class="st">&quot;Precision&quot;</span>]) <span class="op">/</span><span class="st"> </span><span class="kw">nrow</span>(c)</a>
<a class="sourceLine" id="cb70-30" data-line-number="30">    f1 &lt;-<span class="st"> </span><span class="dv">2</span> <span class="op">*</span><span class="st"> </span>((re <span class="op">*</span><span class="st"> </span>pr) <span class="op">/</span><span class="st"> </span>(re <span class="op">+</span><span class="st"> </span>pr))</a>
<a class="sourceLine" id="cb70-31" data-line-number="31">    <span class="kw">return</span>(f1)</a>
<a class="sourceLine" id="cb70-32" data-line-number="32">}</a>
<a class="sourceLine" id="cb70-33" data-line-number="33"></a>
<a class="sourceLine" id="cb70-34" data-line-number="34">human_eth &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>speaker_ethnicity1)</a>
<a class="sourceLine" id="cb70-35" data-line-number="35">algo_eth &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>speaker_ethnicity)</a></code></pre></div>
<p>These are the metrics for each class:</p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb71-1" data-line-number="1">cm &lt;-<span class="st"> </span><span class="kw">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="kw">length</span>(<span class="kw">levels</span>(human_eth)))</a>
<a class="sourceLine" id="cb71-2" data-line-number="2"><span class="cf">for</span> (i <span class="cf">in</span> <span class="kw">seq_along</span>(cm)) {</a>
<a class="sourceLine" id="cb71-3" data-line-number="3">    positive.class &lt;-<span class="st"> </span><span class="kw">levels</span>(human_eth)[i]</a>
<a class="sourceLine" id="cb71-4" data-line-number="4"></a>
<a class="sourceLine" id="cb71-5" data-line-number="5">    cm[[i]] &lt;-<span class="st"> </span><span class="kw">confusionMatrix</span>(human_eth, algo_eth, </a>
<a class="sourceLine" id="cb71-6" data-line-number="6">                               <span class="dt">positive =</span> positive.class)</a>
<a class="sourceLine" id="cb71-7" data-line-number="7">}</a>
<a class="sourceLine" id="cb71-8" data-line-number="8">metrics &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;Precision&quot;</span>, <span class="st">&quot;Recall&quot;</span>, <span class="st">&quot;F1&quot;</span>)</a>
<a class="sourceLine" id="cb71-9" data-line-number="9"></a>
<a class="sourceLine" id="cb71-10" data-line-number="10"><span class="kw">print</span>(cm[[<span class="dv">1</span>]]<span class="op">$</span>byClass[, metrics])</a></code></pre></div>
<pre><code>##                 Precision    Recall        F1
## Class: Asian    1.0000000 1.0000000 1.0000000
## Class: Black    0.9545455 0.9545455 0.9545455
## Class: Hispanic 0.5454545 0.4615385 0.5000000
## Class: Other    0.5000000 0.7500000 0.6000000
## Class: White    0.9666667 0.9666667 0.9666667</code></pre>
<p>This is the overall micro averaged f1 score, which does not take imbalanced class distribution into account:</p>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb73-1" data-line-number="1">micro.f1 &lt;-<span class="st"> </span><span class="kw">get.micro.f1</span>(cm)</a>
<a class="sourceLine" id="cb73-2" data-line-number="2"><span class="kw">print</span>(<span class="kw">paste0</span>(<span class="st">&quot;Micro F1 is: &quot;</span>, <span class="kw">round</span>(micro.f1, <span class="dv">2</span>)))</a></code></pre></div>
<pre><code>## [1] &quot;Micro F1 is: 0.96&quot;</code></pre>
<p>And this is the macro averaged f1 scores, which takes performance for each individual class into account:</p>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb75-1" data-line-number="1">macro.f1 &lt;-<span class="st"> </span><span class="kw">get.macro.f1</span>(cm)</a>
<a class="sourceLine" id="cb75-2" data-line-number="2"><span class="kw">print</span>(<span class="kw">paste0</span>(<span class="st">&quot;Macro F1 is: &quot;</span>, <span class="kw">round</span>(macro.f1, <span class="dv">2</span>)))</a></code></pre></div>
<pre><code>## [1] &quot;Macro F1 is: 0.81&quot;</code></pre>
</div>
<div id="gender-1" class="section level4">
<h4>Gender</h4>
<p>For the binary gender category the metrics for precision, recall and F1 fall between 0.95 and 0.97:</p>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb77-1" data-line-number="1">human_gender &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>speaker_face_gender1)</a>
<a class="sourceLine" id="cb77-2" data-line-number="2">algo_gender &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>speaker_face_gender)</a>
<a class="sourceLine" id="cb77-3" data-line-number="3"></a>
<a class="sourceLine" id="cb77-4" data-line-number="4"><span class="kw">print</span>(<span class="kw">confusionMatrix</span>(human_gender, algo_gender,</a>
<a class="sourceLine" id="cb77-5" data-line-number="5">                      <span class="dt">positive =</span> <span class="st">'female'</span>, <span class="dt">mode =</span> <span class="st">'everything'</span>))</a></code></pre></div>
<pre><code>## Confusion Matrix and Statistics
## 
##           Reference
## Prediction female male
##     female     71    3
##     male        2  124
##                                              
##                Accuracy : 0.975              
##                  95% CI : (0.9426, 0.9918)   
##     No Information Rate : 0.635              
##     P-Value [Acc &gt; NIR] : &lt;0.0000000000000002
##                                              
##                   Kappa : 0.9462             
##                                              
##  Mcnemar's Test P-Value : 1                  
##                                              
##             Sensitivity : 0.9726             
##             Specificity : 0.9764             
##          Pos Pred Value : 0.9595             
##          Neg Pred Value : 0.9841             
##               Precision : 0.9595             
##                  Recall : 0.9726             
##                      F1 : 0.9660             
##              Prevalence : 0.3650             
##          Detection Rate : 0.3550             
##    Detection Prevalence : 0.3700             
##       Balanced Accuracy : 0.9745             
##                                              
##        'Positive' Class : female             
## </code></pre>
</div>
</div>
<div id="predicting-gender-and-racial-categories-with-wru" class="section level3">
<h3>Predicting gender and racial categories with <em>wru</em></h3>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb79-1" data-line-number="1"><span class="co"># extracting surnames</span></a>
<a class="sourceLine" id="cb79-2" data-line-number="2">comb &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">mutate</span>(</a>
<a class="sourceLine" id="cb79-3" data-line-number="3">  <span class="dt">surname =</span> <span class="kw">gsub</span>(<span class="st">&quot;^.* &quot;</span>, <span class="st">&quot;&quot;</span>, speaker),</a>
<a class="sourceLine" id="cb79-4" data-line-number="4">  <span class="dt">first_name =</span> <span class="kw">str_split</span>(speaker, <span class="st">&quot; &quot;</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">map_chr</span>(<span class="dv">1</span>))</a>
<a class="sourceLine" id="cb79-5" data-line-number="5">comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(first_name, surname, speaker)</a></code></pre></div>
<pre><code>## # A tibble: 200 x 3
##    first_name surname       speaker             
##    &lt;chr&gt;      &lt;chr&gt;         &lt;chr&gt;               
##  1 Shane      Koyczan       Shane Koyczan       
##  2 Michael    Dickinson     Michael Dickinson   
##  3 David      Keith         David Keith         
##  4 Paul       Snelgrove     Paul Snelgrove      
##  5 Sean       Carroll       Sean Carroll        
##  6 Bruce      Feiler        Bruce Feiler        
##  7 Victor     Rios          Victor Rios         
##  8 Brian      Skerry        Brian Skerry        
##  9 Philip     Howard        Philip K. Howard    
## 10 Dennis     vanEngelsdorp Dennis vanEngelsdorp
## # … with 190 more rows</code></pre>
<div class="sourceCode" id="cb81"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb81-1" data-line-number="1"><span class="co"># extracting firstnames</span></a></code></pre></div>
<div id="getting-gender-estimates" class="section level4">
<h4>Getting gender estimates</h4>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb82-1" data-line-number="1">name_gender &lt;-<span class="st"> </span><span class="kw">findGivenNames</span>(<span class="kw">unique</span>(comb<span class="op">$</span>first_name),</a>
<a class="sourceLine" id="cb82-2" data-line-number="2">               <span class="dt">textPrepare =</span> <span class="ot">FALSE</span>,  <span class="dt">progress =</span> <span class="ot">FALSE</span>) </a></code></pre></div>
<div class="sourceCode" id="cb83"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb83-1" data-line-number="1">name_gender &lt;-<span class="st"> </span>name_gender <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb83-2" data-line-number="2"><span class="st">                </span><span class="kw">rename</span>(<span class="dt">first_name =</span> name, <span class="dt">gender_pred_name =</span> gender) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb83-3" data-line-number="3"><span class="st">                </span><span class="kw">select</span>(first_name, gender_pred_name)</a>
<a class="sourceLine" id="cb83-4" data-line-number="4"></a>
<a class="sourceLine" id="cb83-5" data-line-number="5">comb &lt;-<span class="st"> </span>comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">left_join</span>(name_gender, <span class="dt">by =</span> <span class="st">'first_name'</span>)</a></code></pre></div>
</div>
<div id="getting-ethnicity-estimates" class="section level4">
<h4>Getting ethnicity estimates</h4>
<p>Based on <a href="https://doi.org/10.1093/pan/mpw001">this paper</a>:</p>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb84-1" data-line-number="1">name_eth &lt;-<span class="st"> </span><span class="kw">predict_race</span>(<span class="dt">voter.file =</span> comb, <span class="dt">surname.only =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<pre><code>## [1] &quot;Proceeding with surname-only predictions...&quot;</code></pre>
<pre><code>## Warning in merge_surnames(voter.file): Probabilities were imputed for 48
## surnames that could not be matched to Census list.</code></pre>
<div class="sourceCode" id="cb87"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb87-1" data-line-number="1">name_pred &lt;-<span class="st"> </span>name_eth <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(<span class="kw">starts_with</span>(<span class="st">'pred'</span>))</a>
<a class="sourceLine" id="cb87-2" data-line-number="2">name_pred_max &lt;-<span class="st"> </span><span class="kw">colnames</span>(name_pred)[<span class="kw">max.col</span>(name_pred ,<span class="dt">ties.method=</span><span class="st">&quot;first&quot;</span>)]</a></code></pre></div>
<div class="sourceCode" id="cb88"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb88-1" data-line-number="1">comb<span class="op">$</span>speaker_ethnicity_surname &lt;-<span class="st">  </span><span class="kw">case_when</span> (</a>
<a class="sourceLine" id="cb88-2" data-line-number="2">                 name_pred_max <span class="op">==</span><span class="st"> 'pred.whi'</span> <span class="op">~</span><span class="st"> 'White'</span>,</a>
<a class="sourceLine" id="cb88-3" data-line-number="3">                 name_pred_max <span class="op">==</span><span class="st"> 'pred.bla'</span> <span class="op">~</span><span class="st"> 'Black'</span>,</a>
<a class="sourceLine" id="cb88-4" data-line-number="4">                  name_pred_max <span class="op">==</span><span class="st"> 'pred.asi'</span> <span class="op">~</span><span class="st"> 'Asian'</span>,</a>
<a class="sourceLine" id="cb88-5" data-line-number="5">                  name_pred_max <span class="op">==</span><span class="st"> 'pred.his'</span> <span class="op">~</span><span class="st"> 'Hispanic'</span>,</a>
<a class="sourceLine" id="cb88-6" data-line-number="6">                 <span class="ot">TRUE</span> <span class="op">~</span><span class="st"> 'Other'</span></a>
<a class="sourceLine" id="cb88-7" data-line-number="7">                )</a>
<a class="sourceLine" id="cb88-8" data-line-number="8">comb <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">count</span>(speaker_ethnicity_surname)</a></code></pre></div>
<pre><code>## # A tibble: 5 x 2
##   speaker_ethnicity_surname     n
##   &lt;chr&gt;                     &lt;int&gt;
## 1 Asian                        19
## 2 Black                         4
## 3 Hispanic                     13
## 4 Other                         1
## 5 White                       163</code></pre>
</div>
</div>
<div id="comparison-name-based-vs-human-annotations" class="section level3">
<h3>Comparison: Name-based vs human annotations</h3>
<p>We can now repeat the process from above by comparing name-based predictions with the human annotations.</p>
<p>For ethnicity, the F1 scores are substantially higher in comparison to the image recognition algorithm:</p>
<div class="sourceCode" id="cb90"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb90-1" data-line-number="1">name_eth &lt;-<span class="st"> </span>comb<span class="op">$</span>speaker_ethnicity_surname <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">as.factor</span>()</a>
<a class="sourceLine" id="cb90-2" data-line-number="2"></a>
<a class="sourceLine" id="cb90-3" data-line-number="3">cm &lt;-<span class="st"> </span><span class="kw">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="kw">length</span>(<span class="kw">levels</span>(human_eth)))</a>
<a class="sourceLine" id="cb90-4" data-line-number="4"><span class="cf">for</span> (i <span class="cf">in</span> <span class="kw">seq_along</span>(cm)) {</a>
<a class="sourceLine" id="cb90-5" data-line-number="5">    positive.class &lt;-<span class="st"> </span><span class="kw">levels</span>(human_eth)[i]</a>
<a class="sourceLine" id="cb90-6" data-line-number="6"></a>
<a class="sourceLine" id="cb90-7" data-line-number="7">    cm[[i]] &lt;-<span class="st"> </span><span class="kw">confusionMatrix</span>(human_eth, name_eth, </a>
<a class="sourceLine" id="cb90-8" data-line-number="8">                               <span class="dt">positive =</span> positive.class)</a>
<a class="sourceLine" id="cb90-9" data-line-number="9">}</a>
<a class="sourceLine" id="cb90-10" data-line-number="10">metrics &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;Precision&quot;</span>, <span class="st">&quot;Recall&quot;</span>, <span class="st">&quot;F1&quot;</span>)</a>
<a class="sourceLine" id="cb90-11" data-line-number="11"></a>
<a class="sourceLine" id="cb90-12" data-line-number="12"><span class="kw">print</span>(cm[[<span class="dv">1</span>]]<span class="op">$</span>byClass[, metrics])</a></code></pre></div>
<pre><code>##                  Precision    Recall        F1
## Class: Asian    0.81818182 0.4736842 0.6000000
## Class: Black    0.09090909 0.5000000 0.1538462
## Class: Hispanic 0.27272727 0.2307692 0.2500000
## Class: Other    0.00000000 0.0000000       NaN
## Class: White    0.90666667 0.8343558 0.8690096</code></pre>
<div class="sourceCode" id="cb92"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb92-1" data-line-number="1">micro.f1 &lt;-<span class="st"> </span><span class="kw">get.micro.f1</span>(cm)</a>
<a class="sourceLine" id="cb92-2" data-line-number="2"><span class="kw">print</span>(<span class="kw">paste0</span>(<span class="st">&quot;Micro F1 is: &quot;</span>, <span class="kw">round</span>(micro.f1, <span class="dv">2</span>)))</a></code></pre></div>
<pre><code>## [1] &quot;Micro F1 is: 0.86&quot;</code></pre>
<div class="sourceCode" id="cb94"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb94-1" data-line-number="1">macro.f1 &lt;-<span class="st"> </span><span class="kw">get.macro.f1</span>(cm)</a>
<a class="sourceLine" id="cb94-2" data-line-number="2"><span class="kw">print</span>(<span class="kw">paste0</span>(<span class="st">&quot;Macro F1 is: &quot;</span>, <span class="kw">round</span>(macro.f1, <span class="dv">2</span>)))</a></code></pre></div>
<pre><code>## [1] &quot;Macro F1 is: 0.41&quot;</code></pre>
<p>Regarding gender, the performance is much closer to the image recognition algorithm, although still slightly worse:</p>
<div class="sourceCode" id="cb96"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb96-1" data-line-number="1">human_gender &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>speaker_face_gender1)</a>
<a class="sourceLine" id="cb96-2" data-line-number="2">name_gender &lt;-<span class="st"> </span><span class="kw">as.factor</span>(comb<span class="op">$</span>gender_pred_name)</a>
<a class="sourceLine" id="cb96-3" data-line-number="3"></a>
<a class="sourceLine" id="cb96-4" data-line-number="4"><span class="kw">print</span>(<span class="kw">confusionMatrix</span>(human_gender, name_gender,</a>
<a class="sourceLine" id="cb96-5" data-line-number="5">                      <span class="dt">positive =</span> <span class="st">'female'</span>, <span class="dt">mode =</span> <span class="st">'everything'</span>))</a></code></pre></div>
<pre><code>## Confusion Matrix and Statistics
## 
##           Reference
## Prediction female male
##     female     66    4
##     male        4  112
##                                              
##                Accuracy : 0.957              
##                  95% CI : (0.917, 0.9813)    
##     No Information Rate : 0.6237             
##     P-Value [Acc &gt; NIR] : &lt;0.0000000000000002
##                                              
##                   Kappa : 0.9084             
##                                              
##  Mcnemar's Test P-Value : 1                  
##                                              
##             Sensitivity : 0.9429             
##             Specificity : 0.9655             
##          Pos Pred Value : 0.9429             
##          Neg Pred Value : 0.9655             
##               Precision : 0.9429             
##                  Recall : 0.9429             
##                      F1 : 0.9429             
##              Prevalence : 0.3763             
##          Detection Rate : 0.3548             
##    Detection Prevalence : 0.3763             
##       Balanced Accuracy : 0.9542             
##                                              
##        'Positive' Class : female             
## </code></pre>
</div>
</div>
<div id="r-environment-info" class="section level2">
<h2>R environment info</h2>
<p>The following cell contains info about the R environment that was used for the analysis:</p>
<div class="sourceCode" id="cb98"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb98-1" data-line-number="1"><span class="kw">sessionInfo</span>()</a></code></pre></div>
<pre><code>## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 18.3
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] AER_1.2-6         survival_2.44-1.1 sandwich_2.5-1   
##  [4] lmtest_0.9-37     zoo_1.8-5         car_3.0-2        
##  [7] carData_3.0-2     forcats_0.4.0     stringr_1.4.0    
## [10] dplyr_0.8.1       purrr_0.3.2       readr_1.3.1      
## [13] tidyr_0.8.3       tibble_2.1.1      tidyverse_1.2.1  
## [16] arm_1.10-1        lme4_1.1-21       texreg_1.36.23   
## [19] psych_1.8.12      scales_1.0.0.9000 stargazer_5.2.2  
## [22] genderizeR_2.1.0  wru_0.1-9         caret_6.0-84     
## [25] lattice_0.20-38   irr_0.84.1        lpSolve_5.6.13   
## [28] sjmisc_2.7.9      sjPlot_2.6.3      MASS_7.3-51.4    
## [31] GGally_1.4.0      ggdendro_0.1-20   ggeffects_0.10.0 
## [34] ggrepel_0.8.1     ggridges_0.5.1    sentimentr_2.7.1 
## [37] stminsights_0.3.0 cowplot_0.9.4     ggplot2_3.1.1    
## [40] textmineR_3.0.4   Matrix_1.2-17     lubridate_1.7.4  
## [43] gridExtra_2.3     reshape2_1.4.3    stm_1.3.3        
## [46] quanteda_1.4.3   
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.1.4           shinydashboard_0.7.1 tidyselect_0.2.5    
##   [4] devtools_2.0.2       munsell_0.5.0        codetools_0.2-16    
##   [7] withr_2.1.2.9000     colorspace_1.4-1     knitr_1.23          
##  [10] rstudioapi_0.10      stats4_3.4.4         labeling_0.3        
##  [13] emmeans_1.3.4        mnormt_1.5-5         polyclip_1.10-0     
##  [16] farver_1.1.0         glmmTMB_0.2.3        rprojroot_1.3-2     
##  [19] vctrs_0.1.0          coda_0.19-2          generics_0.0.2      
##  [22] TH.data_1.0-10       ipred_0.9-9          xfun_0.7            
##  [25] R6_2.4.0             syuzhet_1.0.4        ISOcodes_2019.04.22 
##  [28] reshape_0.8.8        assertthat_0.2.1     promises_1.0.1      
##  [31] multcomp_1.4-8       ggraph_1.0.2         nnet_7.3-12         
##  [34] gtable_0.3.0         processx_3.3.1       tidygraph_1.1.2     
##  [37] timeDate_3043.102    rlang_0.3.4          zeallot_0.1.0       
##  [40] splines_3.4.4        TMB_1.7.15           lazyeval_0.2.1      
##  [43] ModelMetrics_1.2.2   stopwords_0.9.0      shinyBS_0.61        
##  [46] broom_0.5.2          yaml_2.2.0           abind_1.4-5         
##  [49] modelr_0.1.4         backports_1.1.4      httpuv_1.5.1        
##  [52] tools_3.4.4          lava_1.6.5           usethis_1.5.0       
##  [55] ellipsis_0.1.0       RColorBrewer_1.1-2   sessioninfo_1.1.1   
##  [58] Rcpp_1.0.1           plyr_1.8.4           ps_1.3.0            
##  [61] prettyunits_1.0.2    rpart_4.1-15         viridis_0.5.1       
##  [64] haven_2.1.0          survey_3.36          fs_1.3.1            
##  [67] magrittr_1.5         data.table_1.12.2    openxlsx_4.1.0      
##  [70] effects_4.0-2        SnowballC_0.6.0      mvtnorm_1.0-8       
##  [73] pkgload_1.0.2        hms_0.4.2            mime_0.6            
##  [76] evaluate_0.13        xtable_1.8-4         rio_0.5.16          
##  [79] sjstats_0.17.4       readxl_1.3.1         testthat_2.1.1      
##  [82] compiler_3.4.4       crayon_1.3.4         minqa_1.2.4         
##  [85] htmltools_0.3.6      later_0.8.0          Formula_1.2-3       
##  [88] qdapRegex_0.7.2      spacyr_1.0           RcppParallel_4.4.3  
##  [91] DBI_1.0.0            tweenr_1.0.1         sjlabelled_1.0.17   
##  [94] boot_1.3-22          mitools_2.4          cli_1.1.0           
##  [97] parallel_3.4.4       insight_0.3.0        textclean_0.9.3     
## [100] gower_0.2.1          igraph_1.2.4.1       pkgconfig_2.0.2     
## [103] lexicon_1.2.1        foreign_0.8-71       recipes_0.1.5       
## [106] xml2_1.2.0           foreach_1.4.4        RcppProgress_0.4.1  
## [109] estimability_1.3     prodlim_2018.04.18   rvest_0.3.4         
## [112] snakecase_0.11.0     callr_3.2.0          digest_0.6.19       
## [115] rmarkdown_1.13       cellranger_1.1.0     fastmatch_1.1-0     
## [118] curl_3.3             shiny_1.3.2          nloptr_1.2.1        
## [121] nlme_3.1-140         jsonlite_1.6         fansi_0.4.0         
## [124] desc_1.2.0           viridisLite_0.3.0    pillar_1.4.0        
## [127] httr_1.4.0           pkgbuild_1.0.3       glue_1.3.1          
## [130] remotes_2.0.4        bayestestR_0.1.0     zip_2.0.2           
## [133] iterators_1.0.9      ggforce_0.2.2        class_7.3-15        
## [136] stringi_1.4.3        performance_0.1.0    memoise_1.1.0       
## [139] e1071_1.7-1</code></pre>
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